43 citations · 69 across the 18 of their papers we have counts for
6 papers · 1 filter
Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models
Sumeet Singh, Stephen Tu, Vikas Sindhwani
A crucial design decision for any robot learning pipeline is the choice of policy representation: what type of model should be used to generate the next set of robot actions? Owing…
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Allen Z. Ren, Anushri Dixit, Alexandra Bodrova +11
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remai…
Agile Catching with Whole-Body MPC and Blackbox Policy Learning
Saminda Abeyruwan, Alex Bewley, Nicholas M. Boffi +9
We address a benchmark task in agile robotics: catching objects thrown at high-speed. This is a challenging task that involves tracking, intercepting, and cradling a thrown object…
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…
Mnemosyne: Learning to Train Transformers with Transformers
Deepali Jain, Krzysztof Marcin Choromanski, Avinava Dubey +4
In this work, we propose a new class of learnable optimizers, called \textit{Mnemosyne}. It is based on the novel spatio-temporal low-rank implicit attention Transformers that can…
Single-Level Differentiable Contact Simulation
Simon Le Cleac'h, Mac Schwager, Zachary Manchester +3
We present a differentiable formulation of rigid-body contact dynamics for objects and robots represented as compositions of convex primitives. Existing optimization-based approach…