103 citations · 262 across the 20 of their papers we have counts for
21 papers
Cross-Embodiment Transfer via Behavior-Aligned Representations
Ajay Sridhar, Jensen Gao, Jonathan Yang +3
Recent progress in large-scale imitation learning for robot manipulation has been driven by leveraging datasets across a wide range of robot embodiments. However, achieving signifi…
Training Strategies for Efficient Embodied Reasoning
William Chen, Suneel Belkhale, Suvir Mirchandani +4
Robot chain-of-thought reasoning (CoT) -- wherein a model predicts helpful intermediate representations before choosing actions -- provides an effective method for improving the ge…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
A Taxonomy for Evaluating Generalist Robot Manipulation Policies
Jensen Gao, Suneel Belkhale, Sudeep Dasari +3
Machine learning for robot manipulation promises to unlock generalization to novel tasks and environments. But how should we measure the progress of these policies towards generali…
Action-Free Reasoning for Policy Generalization
Jaden Clark, Suvir Mirchandani, Dorsa Sadigh +1
End-to-end imitation learning offers a promising approach for training robot policies. However, generalizing to new settings remains a significant challenge. Although large-scale r…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
So You Think You Can Scale Up Autonomous Robot Data Collection?
Suvir Mirchandani, Suneel Belkhale, Joey Hejna +3
A long-standing goal in robot learning is to develop methods for robots to acquire new skills autonomously. While reinforcement learning (RL) comes with the promise of enabling aut…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…
RT-H: Action Hierarchies Using Language
Suneel Belkhale, Tianli Ding, Ted Xiao +6
Language provides a way to break down complex concepts into digestible pieces. Recent works in robot imitation learning use language-conditioned policies that predict actions given…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Data Quality in Imitation Learning
Suneel Belkhale, Yuchen Cui, Dorsa Sadigh
In supervised learning, the question of data quality and curation has been over-shadowed in recent years by increasingly more powerful and expressive models that can ingest interne…
HYDRA: Hybrid Robot Actions for Imitation Learning
Suneel Belkhale, Yuchen Cui, Dorsa Sadigh
Imitation Learning (IL) is a sample efficient paradigm for robot learning using expert demonstrations. However, policies learned through IL suffer from state distribution shift at…
KITE: Keypoint-Conditioned Policies for Semantic Manipulation
Priya Sundaresan, Suneel Belkhale, Dorsa Sadigh +1
While natural language offers a convenient shared interface for humans and robots, enabling robots to interpret and follow language commands remains a longstanding challenge in man…
Parallel Sampling of Diffusion Models
Andy Shih, Suneel Belkhale, Stefano Ermon +2
Diffusion models are powerful generative models but suffer from slow sampling, often taking 1000 sequential denoising steps for one sample. As a result, considerable efforts have b…
Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding
Priya Sundaresan, Suneel Belkhale, Dorsa Sadigh
Acquiring food items with a fork poses an immense challenge to a robot-assisted feeding system, due to the wide range of material properties and visual appearances present across f…
Learning Bimanual Scooping Policies for Food Acquisition
Jennifer Grannen, Yilin Wu, Suneel Belkhale +1
A robotic feeding system must be able to acquire a variety of foods. Prior bite acquisition works consider single-arm spoon scooping or fork skewering, which do not generalize to f…
In-Mouth Robotic Bite Transfer with Visual and Haptic Sensing
Lorenzo Shaikewitz, Yilin Wu, Suneel Belkhale +3
Assistance during eating is essential for those with severe mobility issues or eating risks. However, dependence on traditional human caregivers is linked to malnutrition, weight l…
PLATO: Predicting Latent Affordances Through Object-Centric Play
Suneel Belkhale, Dorsa Sadigh
Constructing a diverse repertoire of manipulation skills in a scalable fashion remains an unsolved challenge in robotics. One way to address this challenge is with unstructured hum…
Balancing Efficiency and Comfort in Robot-Assisted Bite Transfer
Suneel Belkhale, Ethan K. Gordon, Yuxiao Chen +3
Robot-assisted feeding in household environments is challenging because it requires robots to generate trajectories that effectively bring food items of varying shapes and sizes in…
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
Suneel Belkhale, Rachel Li, Gregory Kahn +3
Transporting suspended payloads is challenging for autonomous aerial vehicles because the payload can cause significant and unpredictable changes to the robot's dynamics. These cha…
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight
Katie Kang, Suneel Belkhale, Gregory Kahn +2
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quanti…