1 citations · 3 across the 7 of their papers we have counts for
10 papers
Scaling Online Exploration for Robot Agents
Runhan Huang, Xiangcheng Zhang, Haoyu Chen +17
Robot learning has advanced by scaling demonstration datasets and model capacity; however, deploying a new robot embodiment or task typically necessitates a dedicated teleoperation…
GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive
Yulin Liu, Lai Wei, Yen-Jen Wang +4
Foundation models and large-scale human data provide rich sources of manipulation intent, but translating this intent into multi-fingered robot behavior remains difficult. Dexterou…
Tactile Curiosity Drives Robot Interaction
Klemens Iten, Alexander Proshkin, Bhavya Sukhija +4
Mastering robot manipulation skills via reinforcement learning (RL) remains largely sample-inefficient. The most common RL algorithms rely on random action sampling to discover new…
Video2Policy: Scaling up Manipulation Tasks in Simulation through Internet Videos
Weirui Ye, Fangchen Liu, Zheng Ding +3
Simulation offers a promising approach for cheaply scaling training data for generalist policies. To scalably generate data from diverse and realistic tasks, existing algorithms ei…
Global-Local Interface for On-Demand Teleoperation
Jianshu Zhou, Boyuan Liang, Junda Huang +2
Teleoperation is a critical method for human-robot interface, holds significant potential for enabling robotic applications in industrial and unstructured environments. Existing te…
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
Bhavya Sukhija, Stelian Coros, Andreas Krause +2
Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms u…
Body Transformer: Leveraging Robot Embodiment for Policy Learning
Carmelo Sferrazza, Dun-Ming Huang, Fangchen Liu +2
In recent years, the transformer architecture has become the de facto standard for machine learning algorithms applied to natural language processing and computer vision. Despite n…
Any-point Trajectory Modeling for Policy Learning
Chuan Wen, Xingyu Lin, John So +4
Learning from demonstration is a powerful method for teaching robots new skills, and having more demonstration data often improves policy learning. However, the high cost of collec…
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
Daiki E. Matsunaga, Jongmin Lee, Jaeseok Yoon +3
One of the main challenges in offline Reinforcement Learning (RL) is the distribution shift that arises from the learned policy deviating from the data collection policy. This is o…
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own
Weirui Ye, Yunsheng Zhang, Haoyang Weng +6
Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For on…