8 citations · 8 across the 2 of their papers we have counts for
7 papers
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…
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…
ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation
ALOHA 2 Team, Jorge Aldaco, Travis Armstrong +23
Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robus…
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention
Abhishek Gupta, Justin Yu, Tony Z. Zhao +5
Reinforcement Learning (RL) algorithms can in principle acquire complex robotic skills by learning from large amounts of data in the real world, collected via trial and error. Howe…
Calibrate Before Use: Improving Few-Shot Performance of Language Models
Tony Z. Zhao, Eric Wallace, Shi Feng +2
GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the cho…
MELD: Meta-Reinforcement Learning from Images via Latent State Models
Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly +2
Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training ta…