In human environments, robots are expected to accomplish a variety of manipulation tasks
given simple natural language instructions. Yet, robotic manipulation is extremely …

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

Diffusion models have demonstrated strong potential for robotic trajectory planning.
However generating coherent trajectories from high-level instructions remains challenging …

We explore different ways to utilize position-based cross-attention in seq2seq networks to
enable length generalization in algorithmic tasks. We show that a simple approach of …

Combining gradient-based trajectory optimization with differentiable physics simulation is an
efficient technique for solving soft-body manipulation problems. Using a well-crafted …

One-shot imitation is to learn a new task from a single demonstration, yet it is a challenging
problem to adopt it for complex tasks with the high domain diversity inherent in a non …

In this work, we demonstrate a method for implementing policy iteration using a large
language model. While the application of foundation models to RL has received …

This work presents In-Context Policy Iteration, an algorithm for performing Reinforcement
Learning (RL), in-context, using foundation models. While the application of foundation …

Grounding the common-sense reasoning of Large Language Models in physical domains
remains a pivotal yet unsolved problem for embodied AI. Whereas prior works have focused …

Affordance detection and pose estimation are of great importance in many robotic
applications. Their combination helps the robot gain an enhanced manipulation capability …