14 citations · 50 across the 8 of their papers we have counts for
9 papers
Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data
Songbo Hu, Qiayuan Liao, Yufeng Chi +4
Humanoid robots still struggle to plan contact-rich whole-body manipulation from egocentric RGB and proprioception. Workhorse learns such manipulation from robot-free human demonst…
mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning
Kevin Zakka, Qiayuan Liao, Brent Yi +3
We present mjlab, a lightweight, open-source framework for robot learning that combines GPU-accelerated simulation with composable environments and minimal setup friction. mjlab ad…
MuJoCo Playground
Kevin Zakka, Baruch Tabanpour, Qiayuan Liao +10
We introduce MuJoCo Playground, a fully open-source framework for robot learning built with MJX, with the express goal of streamlining simulation, training, and sim-to-real transfe…
PianoMime: Learning a Generalist, Dexterous Piano Player from Internet Demonstrations
Cheng Qian, Julen Urain, Kevin Zakka +1
In this work, we introduce PianoMime, a framework for training a piano-playing agent using internet demonstrations. The internet is a promising source of large-scale demonstrations…
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…
RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
Kevin Zakka, Philipp Wu, Laura Smith +8
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics. Reinforcement learning is a promising approach that has achieved impressive…
Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo
Taylor Howell, Nimrod Gileadi, Saran Tunyasuvunakool +3
We introduce MuJoCo MPC (MJPC), an open-source, interactive application and software framework for real-time predictive control, based on MuJoCo physics. MJPC allows the user to ea…
XIRL: Cross-embodiment Inverse Reinforcement Learning
Kevin Zakka, Andy Zeng, Pete Florence +3
We investigate the visual cross-embodiment imitation setting, in which agents learn policies from videos of other agents (such as humans) demonstrating the same task, but with star…
Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly
Kevin Zakka, Andy Zeng, Johnny Lee +1
Is it possible to learn policies for robotic assembly that can generalize to new objects? We explore this idea in the context of the kit assembly task. Since classic methods rely h…