82 citations · 111 across the 17 of their papers we have counts for
17 papers
Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Plans
Homanga Bharadhwaj, Abhinav Gupta, Vikash Kumar +1
We pursue the goal of developing robots that can interact zero-shot with generic unseen objects via a diverse repertoire of manipulation skills and show how passive human videos ca…
RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking
Homanga Bharadhwaj, Jay Vakil, Mohit Sharma +3
The grand aim of having a single robot that can manipulate arbitrary objects in diverse settings is at odds with the paucity of robotics datasets. Acquiring and growing such datase…
Train Offline, Test Online: A Real Robot Learning Benchmark
Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama +9
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across l…
Visual Affordance Prediction for Guiding Robot Exploration
Homanga Bharadhwaj, Abhinav Gupta, Shubham Tulsiani
Motivated by the intuitive understanding humans have about the space of possible interactions, and the ease with which they can generalize this understanding to previously unseen s…
Affordance Diffusion: Synthesizing Hand-Object Interactions
Yufei Ye, Xueting Li, Abhinav Gupta +5
Recent successes in image synthesis are powered by large-scale diffusion models. However, most methods are currently limited to either text- or image-conditioned generation for syn…
Manipulate by Seeing: Creating Manipulation Controllers from Pre-Trained Representations
Jianren Wang, Sudeep Dasari, Mohan Kumar Srirama +2
The field of visual representation learning has seen explosive growth in the past years, but its benefits in robotics have been surprisingly limited so far. Prior work uses generic…
Zero-Shot Robot Manipulation from Passive Human Videos
Homanga Bharadhwaj, Abhinav Gupta, Shubham Tulsiani +1
Can we learn robot manipulation for everyday tasks, only by watching videos of humans doing arbitrary tasks in different unstructured settings? Unlike widely adopted strategies of…
Design of an All-Purpose Terrace Farming Robot
Vibhakar Mohta, Adarsh Patnaik, Shivam Kumar Panda +6
Automation in farming processes is a growing field of research in both academia and industries. A considerable amount of work has been put into this field to develop systems robust…
Last-Mile Embodied Visual Navigation
Justin Wasserman, Karmesh Yadav, Girish Chowdhary +2
Realistic long-horizon tasks like image-goal navigation involve exploratory and exploitative phases. Assigned with an image of the goal, an embodied agent must explore to discover…
Real World Offline Reinforcement Learning with Realistic Data Source
Gaoyue Zhou, Liyiming Ke, Siddhartha Srinivasa +3
Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience. However, current ORL benchmarks are…
All the Feels: A dexterous hand with large-area tactile sensing
Raunaq Bhirangi, Abigail DeFranco, Jacob Adkins +4
High cost and lack of reliability has precluded the widespread adoption of dexterous hands in robotics. Furthermore, the lack of a viable tactile sensor capable of sensing over the…
Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps
Sudeep Dasari, Abhinav Gupta, Vikash Kumar
Learning diverse dexterous manipulation behaviors with assorted objects remains an open grand challenge. While policy learning methods offer a powerful avenue to attack this proble…
Human-to-Robot Imitation in the Wild
Shikhar Bahl, Abhinav Gupta, Deepak Pathak
We approach the problem of learning by watching humans in the wild. While traditional approaches in Imitation and Reinforcement Learning are promising for learning in the real worl…
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?
Yuchen Cui, Scott Niekum, Abhinav Gupta +2
Task specification is at the core of programming autonomous robots. A low-effort modality for task specification is critical for engagement of non-expert end-users and ultimate ado…
R3M: A Universal Visual Representation for Robot Manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar +2
We study how visual representations pre-trained on diverse human video data can enable data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a…
RB2: Robotic Manipulation Benchmarking with a Twist
Sudeep Dasari, Jianren Wang, Joyce Hong +12
Benchmarks offer a scientific way to compare algorithms using objective performance metrics. Good benchmarks have two features: (a) they should be widely useful for many research g…
SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks
Bohan Wu, Feng Xu, Zhanpeng He +2
Recent advances in deep reinforcement learning (RL) have demonstrated its potential to learn complex robotic manipulation tasks. However, RL still requires the robot to collect a l…