22 citations · 81 across the 10 of their papers we have counts for
10 papers
Masked Trajectory Models for Prediction, Representation, and Control
Philipp Wu, Arjun Majumdar, Kevin Stone +4
We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making. MTM takes a trajectory, such as a state-action sequence, and aims to reconstruc…
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…
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
Nicklas Hansen, Yixin Lin, Hao Su +3
Poor sample efficiency continues to be the primary challenge for deployment of deep Reinforcement Learning (RL) algorithms for real-world applications, and in particular for visuo-…
Transformers are Adaptable Task Planners
Vidhi Jain, Yixin Lin, Eric Undersander +2
Every home is different, and every person likes things done in their particular way. Therefore, home robots of the future need to both reason about the sequential nature of day-to-…
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…
Differentiable and Learnable Robot Models
Franziska Meier, Austin Wang, Giovanni Sutanto +2
Building differentiable simulations of physical processes has recently received an increasing amount of attention. Specifically, some efforts develop differentiable robotic physics…
Efficient and Interpretable Robot Manipulation with Graph Neural Networks
Yixin Lin, Austin S. Wang, Eric Undersander +1
Manipulation tasks, like loading a dishwasher, can be seen as a sequence of spatial constraints and relationships between different objects. We aim to discover these rules from dem…
Learning State-Dependent Losses for Inverse Dynamics Learning
Kristen Morse, Neha Das, Yixin Lin +3
Being able to quickly adapt to changes in dynamics is paramount in model-based control for object manipulation tasks. In order to influence fast adaptation of the inverse dynamics…
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm
Giovanni Sutanto, Austin S. Wang, Yixin Lin +4
The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots. RNEA can be framed as a differentiable computational graph, enabling the dy…
Curious iLQR: Resolving Uncertainty in Model-based RL
Sarah Bechtle, Yixin Lin, Akshara Rai +2
Curiosity as a means to explore during reinforcement learning problems has recently become very popular. However, very little progress has been made in utilizing curiosity for lear…