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Found 9 papers, 4 papers with code
Benchmark Real-time Adaptation and Communication Capabilities of Embodied Agent in Collaborative Scenarios
To address this gap, we propose a novel benchmark that assesses agents' reactive adaptability and instantaneous communication capabilities at every step.
Effect of Adaptive Communication Support on Human-AI Collaboration
Leveraging the strong communication capabilities of Large Language Models (LLMs), we propose a Human-Robot Teaming Framework with Multi-Modal Language feedback (HRT-ML), a framework designed to enhance human-robot interaction by adjusting the frequency and content of language-based feedback.
DSBench: How Far Are Data Science Agents to Becoming Data Science Experts?
To bridge this gap, we introduce DSBench, a comprehensive benchmark designed to evaluate data science agents with realistic tasks.
Can Large Language Models Solve Robot Routing?
We systematically investigate the performance of LLMs in robot routing by constructing a dataset with 80 unique robot routing problems across 8 variants in both single and multi-robot settings.
Guaranteed Trust Region Optimization via Two-Phase KL Penalization
Then, we show that introducing a "fixup" phase is sufficient to guarantee a trust region is enforced on every policy update while adding fewer than 5% additional gradient steps in practice.
HyperPPO: A scalable method for finding small policies for robotic control
We demonstrate that the neural policies estimated by HyperPPO are capable of decentralized control of a Crazyflie2. 1 quadrotor.
Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning
In this work, we propose an end-to-end DRL approach to control quadrotor swarms in environments with obstacles.
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control
In addition to speed, such simulators need to model the physics of the robots and their interaction with the environment to a level acceptable for transferring policies learned in simulation to reality.
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.
