Progress 12/01/23 to 11/30/24
Outputs
Target Audience:Research in AIFARMS and, by extension, this award has been disseminated to various target audiences; we have published several papers in peer-reviewed journals and presented at professional meetings attended by other academic scientists and industry partners. Through their engagement in AIFARMS, researchers, graduate students, and postdoctoral receive training. The AIFARMS team has also reached farmers, extension agents, and industry through demonstrations and field days. We have targeted the public as an audience for our activities through appearances on local TV, local radio, national farmer conference/workshops and the Farm Progress show. AIFARMS has been featured on several national media outlets, broadening our target audience to include members of the wider public outside of agriculture. We are collaborating with several companies interested in our research to demonstrate and test practical applications and adapt our approaches to meet their needs. Changes/Problems:Of the original objectives stated in the research proposal, we have made progress on 3 of the 4. When the grant was originally written, the objective "Address the problem of nutrient leaching into U.S. waterways from agriculture runoff with combined machine learning and mobile measurement techniques to monitor and predict nutrient leaching" was to be led by the post-doc at the University of Chicago. The post-doc has since left her position and the person who filled the position has not expressed interest in continuing the collaboration. We have switched directions to focus on automation and robotic applications in agriculture, expanding the original objective of "1. Develop low-cost, autonomous robotics technologies specifically focused around autonomous weeding." We will make some progress on this objective in the summer of 2025 when a Tuskegee University student plans to visit Bonn. What opportunities for training and professional development has the project provided?Tenure-track faculty and senior researchers: AIFARMS has 40 senior researchers and faculty spanning universities and disciplines. Six faculty were engaged in the international collaboration during Graduate and postdoc training: 3 graduate students and 1 postdoctoral scientists work on the international collaboration across 3 aims. Trainees span CS and crop science, with significant interactions between disciplines throughout regular meetings, special events, and social events throughout the year. One PhenoRob graduate student spent the summer at UIUC. Workforce Development: Many of the projects described above are significant investments in training the next generation of agriculture and AI professionals. In addition to those programs, AIFARMS with supplement from PhenoRob hosted a summer AI foundry. The program's goal is to increase competency in tackling agriculture challenges with AI during a week-long hands-on course designed for graduate students with limited experience in machine learning. Following the 4-day training, students participate in a Hackathon sponsored by local AgTech companies. The program is split between AI topics (ML and CV), covered in the morning lectures, and agriculture applications (livestock management, crop improvement, and robotics) covered in the afternoon. The robotics section included material pulled from PhenoRob sources that was provided to AIFARMS for us in this course. How have the results been disseminated to communities of interest?Engagement with the Research Community: The researchers on the team have at least 3 publications and participated in at least 3 conferences. Throughout the year AIFARMS team had the opportunity to showcase the impact and accomplishments of the institute through multiple public events and essential meetings. Each opportunity we showcase the work with PhenoRob in addition to that of the institute as a whole. These events enabled the team to make connections with a broader community, initiating knowledge transfer of AIFARMS outcomes and promoting the federal investment in AI for Agriculture research. A few key events are described below. AIFARMS was asked by NSF to participate in the AI Expo. Dr. Adve and Dr. Wedow presented at the AI Expo in D.C. to emphasize the role of foundational AI research in agriculture outcomes. In January 2024, Dr. Wedow presented for the Farm Foundation on the mission and accomplishments of AIFARMS. In March 2024, AIFARMS, as part of the Center for Digital Agriculture, participated in the Center's annual conference with many members of the region's AgTech industry and academics from UIUC and neighboring universities present. Engagement with Government: In February 2024, Dr. Wedow attended the USDA 100th annual Agricultural Outlook Forum. While there she spoke with Josh Stull (USDA-NIFA) and Davida Tengey (USDA International Engagement Officer) regarding the AI for Ag. Institutes, including the engagement with PhenoRob. This meeting led to the 5 institutes meeting with Dr. Tengey to discuss our international engagements. In October of 2024, Dr. Adve and Wedow meet with Dr. Misra to present the work of AIFARMS Institute, included in this presentation was the collaboration with PhenoRob and some of the collaborative projects. Talissa Floriani Souza was selected for the AGBT Agricultural Next-Gen leadership for her work with PhenoRob. She will be presenting her work at the next conference. What do you plan to do during the next reporting period to accomplish the goals?We will be sending another batch of graduate students to PhenoRob during the summer and spring semesters. Students will target all 4 objectives proposed in the orginal grant, including the untouched objective "Address the problem of nutrient leaching into U.S. waterways from agriculture runoff with combined machine learning and mobile measurement techniques to monitor and predict nutrient leaching". In addition, we will be sending multiple faculty to PhenoRob to complete short-term visits and encourage collaborative project development between the two institutes. Below is a list of students and faculty who will be sent to visit Bonn in 2025. PhD Student Garvita Allabadi, Vikram Adve advisee. We propose to leverage structured attributes, such as visual, spatial, and semantic properties to enhance the reasoning capabilities of large vision language models for different downstream tasks. By integrating these attributes, models can develop a richer understanding of their environment, enabling more effective zero-shot adaptation to novel settings. One possible application of our approach is Zero-Shot Semantic Navigation, where an agent must navigate an unseen environment by reasoning over the scene. Unlike traditional methods that rely primarily on object localization and classification, our approach would enable models to reason and determine the best course of action based on the detailed scene attributes. PhD Student John Meyers, Nicolas Martin advisee. Automated Disease Scouting: Deploy drones equipped with multispectral and hyperspectral sensors to capture high-resolution images of crop fields. Utilize deep learning models to process these images, enabling early detection of disease symptoms and differentiation between healthy and infected plants. Yield Impact Prediction: Develop AI-driven models that integrate drone imagery, weather data, and soil conditions to estimate disease progression and predict potential yield losses. This will assist farmers in making data-driven decisions regarding targeted interventions. Two PhD students from Tuskegee University, both advised by Joesph Quansah will be traveling to Bonn this summer. The first project will examine how smallholder farmers often operate on small-scale farms that are more sensitive to environmental changes. Understanding the ecological-economic trade-offs of technology adoption can help design systems that benefit these farmers without exacerbating existing inequalities. The second project will explore tools for decision making support in agriculture to enhance the understanding of soil moisture variations and their impact on crop health and yield to optimize irrigation schedules for farmers. Integrating UAV and satellite data will improve the interpretation of drought stress and refine experimental design approaches for better irrigation management. The following faculty all plan to visit PhenoRob over the summer and fall to expand collaborations: Gregory Bernard (Tuskegee), Joesph Quansah (Tuskegee), Carl Bernacchi (UIUC), Alex Lipka (UIUC), Jessica Wedow (UIUC), and Vikram Adve (UIUC). Finally, A PhD student in Hugo Storm's lab (PhenoRob) will be participating in a student exchange this summer to spend time in Madhu Khanna's lab at UIUC. The PhenoRob student will explore adoption of innovative technologies in the agricultural industry. In particular, understanding why autonomous vehicles, for instance in Europe, have yet to meet expectations, as well as the stagnant adoption trends among farmers and the restrictive behavioron the manufacturer side. They will investigate theintermediaries between manufacturers and farmers through qualitative research, including interviews with both parties. In addition to the student and faculty exchange, the DigiCrop network (officially launched in 2023) will be hosting a virtual DigiCrop conference. DIGICROP 2025 (digicrop.de) is a scientific event addressing an international and interdisciplinary audience working at the intersection of engineering, robotics, computer science, crop sciences, agricultural sciences, phenotyping, and economics. Contributions will be made that develop, propose, use, or evaluate new digital technologies towards sustainability in the context of crop production, crop breeding, biodiversity, or agro-ecosystems. DIGICROP 2025 will offer a mix of keynote presentations and video talks based on submitted contributions. The conference aims to stimulate scientific discussions among the participants and disseminate results across disciplinary boundaries. Presentations by industry representatives are welcome. Format: DIGICROP 2025 will take place online, consisting of pre-recorded streamed video talks and live-streamed panel discussions. Talks will be also available online from June 10 on. Live panel discussions will take place on July 8-10 (5-9 pm, CEST) using a video conferencing tool. All participants and presenters are free of charge to attend the event.
Impacts
What was accomplished under these goals?
Between November 2023 and November 2024, 3 PhD students were sent to Bonn, Germany to complete international research experiences. Each of the 3 students were paired with a PhenoRob Cluster of Excellence faculty member to supplement their research goals related to one of the 4 points listed in the major goals of the project and supplement work supporting the AIFARMS AI Institute. In addition to the 3 PhD students sent to PhenoRob, economic and public policy research thrust completed a research publication with PhenoRob Cluster of Excellence. Details of all projects are outlines below. We have made changes to the original objectives since the proposal was written, these have been described in the changes and problems section of this report. Of the original objectives the following 3 have been acted on. Objective 1: Autonomous Robotics for Agriculture This goal expanded to include autonomous navigation and robotic manipulation. Two AIFARMS PhD students went to PhenoRob, and a Bonn PhD student participated in an exchange at UIUC. Arun Narenthiran, AIFARMS PhD student advised by Girish Chowdhary, spent the late summer and fall at PhenoRob completing field experiments for his PhD dissertation. A project summary is as follows: Under-canopy agricultural robots can enable various applications like precise monitoring, spraying, weeding, and plant manipulation tasks throughout the growing season. Autonomous navigation under the canopy is challenging due to the degradation in accuracy of RTK-GPS and the large variability in the visual appearance of the scene over time. In prior work (CropFollow++), we developed a supervised learning-based perception system with semantic keypoint representation and deployed this in various field conditions. A large number of failures of this system can be attributed to the inability of the perception model to adapt to the domain shift encountered during deployment. In this project, we have developed a self-supervised online adaptation method for adapting the semantic keypoint representation using a visual foundational model, geometric prior, and pseudo labeling. Our preliminary experiments show that with minimal data and fine-tuning of parameters, the keypoint prediction model trained with labels on the source domain can be adapted in a self-supervised manner to various challenging target domains onboard the robot computer using our method. This can enable fully autonomous row-following capability in under-canopy robots across fields and crops without requiring human intervention.Collaborators on this project include: Federico Magistri (PhD student, PhenoRob), Jens Behley (Post-doc, PhenoRob), Cyrill Stachniss (PhenoRob, director), and Girish Chowdhary (AIFARMS). This collaboration has resulted in an IROS workshop paper and IEEE Robotics and Autonomation publication. Arun was also recognized with 2nd place for Best Paper Award at the IROS 2024 workshop on AI and Robotics for Future Farming in UAE. Shaoxiong Yao, AIFARMS PhD student advised by Kris Hauser, spent the spring at PhenoRob completing glasshouse experiments for plant manipulation studies. A project summary is as follows: To meet the rising demand for fruits, effective monitoring of crop growth is essential for accurate yield estimation and selective harvesting. Automation of these labor-intensive tasks through robotics offers a promising solution. However, agricultural crops often have dense foliage, and occlusion from leaves significantly hampers accurate fruit estimation. Current AI-based approaches, including shape completion techniques, face challenges in scenarios with substantial leaf occlusion, resulting in uncertain estimates of fruit size, pose, and shape. Robotic systems can actively manipulate plants, such as by repositioning leaves, to improve fruit visibility and enhance estimation accuracy. In this project, we developed a robotic system capable of autonomously planning manipulation actions to minimize fruit occlusion. Experimental results in a controlled lab environment demonstrated that our system achieves an average of 95% maximum fruit visibility, improving the accuracy of fruit shape and location estimates by 40%.Scientific contributions: Two key techniques enable our active fruit estimation system. We first develop a novel scene-consistent shape completion method that estimates the size, pose, and shape of fruits from partial observations, using the completed shape as a prior for visibility prediction and collision avoidance. Additionally, we introduced a precise leaf deformation model, based on a variant of the as-rigid-as-possible shape deformation model to simulate plant deformation. This model accurately predicts the consequences of manipulation actions on leaves, with the deformation energy serving as a predictive feature that guides the selection of safe actions, thereby minimizing potential damage to the plant. This project is on going and details of future directions and in plans for next reporting period. Collaborators on this project include: Maren Bennewitz (PhenoRob), Sicong Pan (PhD student, PhenoRob), and Kris Hauser (AIFARMS). This collaboration has resulted in a conference presentation and ICRA 2025 conference publication. Objective 2: Genomic and Genetic Advances in Crop Health This objective shifted to focus on genomic improvements. One PhD student was sent to PhenoRob, and faculty member Alexander Lipka had a short visit while on sabbatical. Talissa Floriani Souza, AIFARMS PhD student advised by Alexander Lipka, spent the summer at PhenoRob expanding her knowledge of plant genetics. Talissa completed internship under the supervision of Professors Dr. Peng Yu from PhenoRob and Dr. Frank Hochholdinger at the Crop Functional Genomics Lab and Root Functional Biology, at INRES (Institute of Plant Sciences and Resource Conservation). The research in their lab focuses on root genetics in maize and Arabidopsis, with an emphasis on understanding critical genetic components such as heterosis. Throughout the internship, Talissa engaged with students and provided feedback on their projects. With her background in quantitative and statistical genetics,it was valuable to observe their presentations and hypotheses, giving a broader perspective on the implementation of GWAS and QTL analysis. Her work specifically focused on integrating machine learning techniques developed in the Lipka lab into existing genetic research frameworks. This approach opened the possibility of applying these frameworks to transcriptomic data, with a particular focus on identifying rare variants that significantly contribute to agronomic traits, root architecture, and responses to abiotic stress. One of the key outcomes was the development of a novel methodology for applying machine learning models to predict phenotypic traits from genotypic data. Talissa advanced this methodology, which is now being tested in various simulation scenarios. This approach holds potential as a valuable tool in both basic research and applied plant breeding, especially in identifying variants with a strong impact on quantitative traits in crops. Objective 3: Economic Incentives for AI Adoption Progress was most significant in 2023, resulting in a published paper. Madhu Khanna and Shadi Atallah (AIFARMS) collaborated with Hugo Storm and Thomas Heckelei (PhenoRob).The post-doc, Linghui Wu, who did the bulk of the progress, has taken a faculty job. Continued collaborations exist between the research groups, and ideas are regularly exchanged.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Shaoxiong Yao*, Sicong Pan*, Maren Bennewitz and Kris Hauser. Safe Leaf Manipulation for Accurate Shape and Pose Estimation of Occluded Fruits , IROS2024 Workshop on Agricultural Robotics for a Sustainable Future, 2024
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Sivakumar, A., Magistri, F., Gasparino, M., Behley, J., Stachniss, C., & Chowdhary, G. (2024). AdaCropFollow: Self-Supervised Online Adaptation for Visual Under-Canopy Navigation. In Workshop on Robotics & AI for Future Farming at International Conference on Intelligent Robots and Systems (IROS) 2024.
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Khanna, M., S. Atallah, Hugo Storm, Thomas Heckelei, Linghui Wu. Economics of Adoption of Artificial Intelligence-Based Digital Technologies for a Sustainable Agriculture. Annual Review of Resource Economics. Vol. 16. DOI: 10.1146/annurev-resource-101623-092515.
- Type:
Conference Papers and Presentations
Status:
Submitted
Year Published:
2024
Citation:
Quantitative Genetics and Genomics Gordon Research Conference, Lucca, Barga, Italy. Enhancing genotype-phenotype associations and unveiling rare variants with machine learning , Floriani T, El-Kebir M, Fernandes SB, Leakey A, and Lipka AE. February 16-20, 2025.
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Progress 12/01/22 to 11/30/23
Outputs
Target Audience:Research in the joint AIFARMS - PhenoRob collaboration has been disseminated to various target audiences, we have presented at professional and internal meetings attended by other academic scientists and industry partners. Through their engagement in AIFARMS research graduate students are receiving international research experience. We have been collaborating with other research university within Europe, to form a joint research network. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?AIFARMS and PhenoRob are planning multiple student internships for UIUC students during the summer of 2024. PhenoRob students will be visiting UIUC, with one planned for the summer of 2024. 1. Two PhD students in Kris Hauser's lab, Shaoxiong Yao and Joao Correia Marques, were supported by this project, and two master's students were partially supported by this project (Baoyu Li and Jing-Chen Peng). Joao Correia Marques had the opportunity to attend the IEEE ICRA Conference, the flagship robotics conference of the IEEE, which presented professional networking and development opportunities. 2. Floriana, PhD student in Dr. Lipka's lab, will conduct a summer internship at the University of Bonn. 3. Sivakumar, PhD student in Dr. Chowdhary's lab, will conduct a summer internship at the University of Bonn 4. Dr. Wu, a postdoc Madhu Khanna's lab had the opportunity to work with the PhenoRob team to work on a joint paper, to be published this year. 5. Dr. Kuhl, a postdoc at Argonne National Lab, went with the AIFARMS team to visit the PhenoRob campus. This gave her networking opportunities. How have the results been disseminated to communities of interest?Hauser lab: One conference paper, two workshop papers, and one pending conference paper were published as part of this work. The conference papers appear in stringently peer-reviewed robotics venues. Another conference paper has been submitted to the Robotics: Science and Systems 2024 conference(see citaitons in publications). Lipka lab: Ms. Floriani gave a talk at PAG (see citaiton in publications). Khanna lab: Two papers in press to be submitted in 2024. The joint AIFARMS - PhenoRob work has been presented at multiple internal events including our advisory board reviews and annual conferences. What do you plan to do during the next reporting period to accomplish the goals?Three students will be visiting the University of Bonn this summer for an internship program. Dr. Hausers lab will be planning a student exchange with Bonn graduate student Sicong Pan planning to visit AIFARMS, and UIUC graduate student Shaoxiong Yao planning to visit PhenoRob in spring 2024 to pursue collaborative research. The next reporting period will focus on "closing the loop" between currently developed perception algorithms with task-oriented planning algorithms. A collaboration with PhenoRob will be developed through student exchange and joint research projects. An AIFARMS-funded Ph.D. student advised by Prof. Lipka, will conduct a summer internship study at the University of Bonn from approximately May-August 2024.Alex Lipka will be visiting the PhenoRob campus early in 2024 to future expand his collaborative projects. He will visit with several faculty members to discuss common research interest and potential next steps for joint US - DE research proposals. Plans for this reserach proposal can be found in the what was accomplished section above. Student exchange between Dr. Chowdhary and Dr. Stachniss (or similar lab) at PhenoRob in the summer of 2024.Plans for this reserach proposal can be found in the what was accomplished section above. Dr. Khanna and team will publish a joint reserach paper with their PhenoRob partners. The leadership team will identify a joint proposal opportunity to pursue in 2024. We will continue to explore new and continued research directions. Our proposal had proposed the idea for a joint community hub. We have started this process with the formation of the DigiCrop network. The next stop will be to meet with all collaborators and come with common goals and proposal directions. Towards the end of 2024, we will plan another delegation trip to PhenoRob or one of the DigiCrop partner institutions to host collaborative meetings.
Impacts
What was accomplished under these goals?
In November 2022, a delegation from AIFARMS, including Tuskegee University and the University of Chicago, traveled to the University of Bonn to launch a new partnership with PhenoRob Cluster of Excellence. The two partners met for a week of detailed research discussions, facility tours, and sharing of research motivations to outline future collaborations. Preliminary discussions occurred for a joint research network centered around a common place to disseminate knowledge related to common areas of interest. A few highlights from the visit to Germany are listed below. The autonomous farming thrust (Vikram Adve and Girish Chowdary) will collaborate with Chris McCool, Cyrill Stachniss, and partner labs in weeding robotics and plant manipulation. PhenoRob has launched two startup companies in the weeding robot space. AIFARMS hopes to expand on the knowledge PhenoRob has and apply it to autonomous weeding for row crops. Members of the environmental resilience thrust (Gregory Bernard) will collaborate with many PhenoRob researchers in remote sensing, disease detection, and hyperspectral imaging for increased adaptation to climate extremes. Alex Kuhl (AIFARMS postdoc for Thrust 4) met with multiple members of PhenoRob's soils health and management focus area. Many potential areas of collaboration were discussed, including sharing nutrient flux models and integrating meetings. Madhu Khanna and Shadi Atallah plan to work on a joint review paper, graduate student and postdoc workshop, and host joint international meetings on technology adoption and policy between the US and EU systems. Jülich Forschungszentrum - AIFARMS members visited the Jülich research center to see their plant phenotyping facilities and speak with researchers on areas of collaboration centered around high-throughput phenotyping. A delegation from AIFARMS was invited to visit the Sugar Beet Research Facility (IFZ) in Gottingen, Germany. While at IFZ, Professor Mahlein and her research group shared some of the research goals and highlights, as well as a tour of their greenhouse facilities, drone, and robot technologies used to study plant resilience in the face of disease, pests, and climate changes. In the fall of 2023, AIFARMS in conjunction with CDA, PhenoRob Cluster of Excellence, Wageningen University, and ETH Zurich launched the joint DigiCrop network. AIFARMS is excited to follow the new research directions and opportunities this network will generate. The DigiCrop network generated a common charter with goals, measures, timeline, governance, and next steps for a successful partnership. In September of 2023, representatives from PhenoRob visited the UIUC campus. While here they meet with several members of the AIFARMS team, toured farms, and talked with administrative officials at UIUC. Meetings with Alex Lipka and Kris Hauser sparked new student research projects that are detailed later in this report. The PhenoRob and AIFARMS leadership discussed plans for 2024, including joint research efforts, student exchange, and continued support of the DigiCrop network. Individual research accomplishments: Throughout the course of 2023, Madhu Khanna and Shadi Atallah have met with the PhenoRob team on a regular basis to write a joint research paper for the Annual Review of Resource Economics. The paper titled "Economics of Adoption of Artificial Intelligence-Based Technologies for a Sustainable Agriculture" is being co-authored with Thomas Heckelei and Hugo Storm from PhenoRob. Khanna and Atallah are also collaborating on a perspective piece with Hugo Storm titled "Steering Digital Technology Adoption and Usage Towards a Safe and Just Operating Space". These papers will be submitted in early 2024. Kris Hauser, member of the autonomous farming thrust, has been partnering with members of the 'Humanoid Robots Lab' at Bonn for plant manipulation with robotic arms to perform active crop reconstruction and mapping. The goals of the plant manipulation work, is to study AI methods that are needed enable agricultural robots to reliably perform plant manipulation tasks, such as picking and inspection, in fields and glasshouses. The main focus of this reporting period was to investigate methods for fusing vision and touch data to estimate force responses when interacting with plants. This is a first step toward "closing the loop" in sensing and control by providing robots the ability to "feel" branches and other structures while performing agricultural tasks. Novel tactile estimation techniques were developed, as well as few-shot learning techniques that allow a robot's predictions to adapt to new scenarios with little data. Alex Lipka, member of the environmental resilience thrust, has been partnering with labs at the Unviersity of Bonn and the PhenoRob team. The primary goal of Ms. Floriani's visit to the University of Bonn, is to apply state-of-the-art AI-based Genome-Wide Association Studies (GWAS) approaches. This application will investigate high-throughput maize root traits elucidated in Prof. Yu's (located at the University of Bonn) research. The specific research area involves identifying rare variants contributing to enhanced crop resiliency. This investigation aligns with the broader research objectives of Prof. Yu and the Lipka Lab. Applying AI-based GWAS methodologies will be a powerful analytical tool to scrutinize and interpret these traits effectively. Finally, Girish Chowdhary and students have been talking with multiple robotics experts at PhenoRob. The labs have had discussions on how they can Evaluate the performance of our vision-based under-canopy navigation system in field conditions at Bonn. They hope to Develop unsupervised domain adaptation for our vision-based navigation system to adapt without human labeling to varying field conditions and test in real field conditions
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
S. Yao and K. Hauser. Estimating Tactile Models of Heterogeneous Deformable Objects in Real Time. IEEE International Conference on Robotics and Automation (ICRA), May 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
R. Qiu, P. Chen, W. Sun, Y.-X. Wang, and K. Hauser. GAPS: Few-Shot Incremental Semantic Segmentation via Guided Copy-Paste Synthesis. CVPR 2023 Workshop on Learning with Limited Labelled Data, June 19, 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
B. Li, W. Edwards, and K. Hauser. Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning. In ICRA 2023 Workshop on Effective Representations, Abstractions, and Priors for Robot Learning (RAP4Robots), May 29, 2023.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
Invited Talk: J.-C. Peng, S. Yao, and K. Hauser. 3D Force and Contact Estimation for a Soft-Bubble Visuotactile Sensor. International Conference on Robotics and Automation (ICRA), May 2024.
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