48 citations · 149 across the 20 of their papers we have counts for
20 papers
Tether: Autonomous Functional Play with Correspondence-Driven Trajectory Warping
William Liang, Sam Wang, Hung-Ju Wang +3
The ability to conduct and learn from interaction and experience is a central challenge in robotics, offering a scalable alternative to labor-intensive human demonstrations. Howeve…
Vision Language Models are In-Context Value Learners
Yecheng Jason Ma, Joey Hejna, Ayzaan Wahid +15
Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal…
Eurekaverse: Environment Curriculum Generation via Large Language Models
William Liang, Sam Wang, Hung-Ju Wang +3
Recent work has demonstrated that a promising strategy for teaching robots a wide range of complex skills is by training them on a curriculum of progressively more challenging envi…
On-Robot Reinforcement Learning with Goal-Contrastive Rewards
Ondrej Biza, Thomas Weng, Lingfeng Sun +6
Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-r…
Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model
Long Le, Jason Xie, William Liang +7
Interactive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation. However, creating these articulated objects…
DrEureka: Language Model Guided Sim-To-Real Transfer
Yecheng Jason Ma, William Liang, Hung-Ju Wang +5
Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. However, sim-to-real approaches typically rely on manual…
Composing Pre-Trained Object-Centric Representations for Robotics From "What" and "Where" Foundation Models
Junyao Shi, Jianing Qian, Yecheng Jason Ma +1
There have recently been large advances both in pre-training visual representations for robotic control and segmenting unknown category objects in general images. To leverage these…
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…
Universal Visual Decomposer: Long-Horizon Manipulation Made Easy
Zichen Zhang, Yunshuang Li, Osbert Bastani +4
Real-world robotic tasks stretch over extended horizons and encompass multiple stages. Learning long-horizon manipulation tasks, however, is a long-standing challenge, and demands…
Eureka: Human-Level Reward Design via Coding Large Language Models
Yecheng Jason Ma, William Liang, Guanzhi Wang +6
Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation ta…
LIV: Language-Image Representations and Rewards for Robotic Control
Yecheng Jason Ma, William Liang, Vaidehi Som +4
We present Language-Image Value learning (LIV), a unified objective for vision-language representation and reward learning from action-free videos with text annotations. Exploiting…
TOM: Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching
Yecheng Jason Ma, Kausik Sivakumar, Jason Yan +2
Standard model-based reinforcement learning (MBRL) approaches fit a transition model of the environment to all past experience, but this wastes model capacity on data that is irrel…
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Arjun Majumdar, Karmesh Yadav, Sergio Arnaud +12
We present the largest and most comprehensive empirical study of pre-trained visual representations (PVRs) or visual 'foundation models' for Embodied AI. First, we curate CortexBen…
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman +3
Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost an…
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via -Advantage Regression
Yecheng Jason Ma, Jason Yan, Dinesh Jayaraman +1
Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets. We propose $\text…
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman +1
We propose State Matching Offline DIstribution Correction Estimation (SMODICE), a novel and versatile regression-based offline imitation learning (IL) algorithm derived via state-o…
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning
Yecheng Jason Ma, Andrew Shen, Osbert Bastani +1
Reinforcement Learning (RL) agents in the real world must satisfy safety constraints in addition to maximizing a reward objective. Model-based RL algorithms hold promise for reduci…
Safe Human-Interactive Control via Shielding
Jeevana Priya Inala, Yecheng Jason Ma, Osbert Bastani +2
Ensuring safety for human-interactive robotics is important due to the potential for human injury. The key challenge is defining safety in a way that accounts for the complex range…
State Relevance for Off-Policy Evaluation
Simon P. Shen, Yecheng Jason Ma, Omer Gottesman +1
Importance sampling-based estimators for off-policy evaluation (OPE) are valued for their simplicity, unbiasedness, and reliance on relatively few assumptions. However, the varianc…
Likelihood-Based Diverse Sampling for Trajectory Forecasting
Yecheng Jason Ma, Jeevana Priya Inala, Dinesh Jayaraman +1
Forecasting complex vehicle and pedestrian multi-modal distributions requires powerful probabilistic approaches. Normalizing flows (NF) have recently emerged as an attractive tool…