Search Results for author:
Found 12 papers, 2 papers with code
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions.
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models
We propose Generation to Simulation (Gen2Sim), a method for scaling up robot skill learning in simulation by automating generation of 3D assets, task descriptions, task decompositions and reward functions using large pre-trained generative models of language and vision.
Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning.
Ranked #2 on
Robot Manipulation
on RLBench
Towards Generalist Robots: A Promising Paradigm via Generative Simulation
This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots.
Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement
Language is compositional; an instruction can express multiple relation constraints to hold among objects in a scene that a robot is tasked to rearrange.
SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments
Existing work has typically been tailored for particular environments or representations.
FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation
We identify several challenges for fluid manipulation learning by evaluating a set of reinforcement learning and trajectory optimization methods on our platform.
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks
We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent's interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system.
HyperDynamics: Generating Expert Dynamics Models by Observation
We propose HyperDynamics, a framework that conditions on an agent’s interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system.
3D-OES: Viewpoint-Invariant Object-Factorized Environment Simulators
Object motion predictions are computed by a graph neural network that operates over the object features extracted from the 3D neural scene representation.
