2.4k citations · 8.4k across the 129 of their papers we have counts for
9 papers · 1 filter
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
Zhiwei Liu, Weiran Yao, Jianguo Zhang +12
The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to generate actions with its core…
REX: Rapid Exploration and eXploitation for AI Agents
Rithesh Murthy, Shelby Heinecke, Juan Carlos Niebles +12
In this paper, we propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. Existing AutoGPT-style techniques have inherent limitations, such as…
iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes
Bokui Shen, Fei Xia, Chengshu Li +12
We present iGibson 1.0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interac…
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter
Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni +4
When searching for objects in cluttered environments, it is often necessary to perform complex interactions in order to move occluding objects out of the way and fully reveal the o…
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
Fei Xia, Chengshu Li, Roberto Martín-Martín +3
Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks. We propose to lift the action s…
Regression Planning Networks
Danfei Xu, Roberto Martín-Martín, De-An Huang +3
Recent learning-to-plan methods have shown promising results on planning directly from observation space. Yet, their ability to plan for long-horizon tasks is limited by the accura…
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning
De-An Huang, Danfei Xu, Yuke Zhu +4
We address one-shot imitation learning, where the goal is to execute a previously unseen task based on a single demonstration. While there has been exciting progress in this direct…
Gibson Env: Real-World Perception for Embodied Agents
Fei Xia, Amir Zamir, Zhi-Yang He +3
Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently l…
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
Danfei Xu, Suraj Nair, Yuke Zhu +4
In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program in…