40 citations · 80 across the 36 of their papers we have counts for
13 papers · 1 filter
Prioritized Rollouts for Efficient World Model-based Vision-Language-Action Policy Optimization
Yifei Sheng, Haoxiang Ren, Zhilong Zhang +8
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for embodied intelligence, but fine-tuning them with reinforcement learning (RL) remains constrained by the…
CoCoBench: A Cooperative Coordination Benchmark for Embodied Multi-Agent Task Planning
Yang Chen, Ye-Xin Xie, Lirong Che +8
Agent systems powered by multimodal large language models (MLLMs) have advanced rapidly in recent years, yet existing embodied-agent benchmarks still lack fine-grained diagnostics…
GigaBrain-0.7: Scaling Embodied Foundation Models to Emergent Capabilities with a Three-System Architecture
GigaBrain Team, Angen Ye, Axiang Sun +56
Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task completion in structured sett…
Efficient Test-Time Optimization for Multi-Agent Proof Autoformalization
Tian-Shuo Liu, Shiyuan Zhang, Zijie Geng +5
Full-proof autoformalization bridges extensive mathematical proofs in natural language with formally validated reasoning, offering a pathway to elevate the ceiling of verifiable ma…
Offline Multi-agent Continual Cooperation via Skill Partition and Reuse
Yuchen Xiao, Lei Yuan, Ruiqi Xue +2
Extracting skills from multi-agent offline dataset improves learning efficiency via sharing task-invariant coordination skills among tasks. In settings where tasks occur sequential…
Provably Efficient Policy-Reward Co-Pretraining for Adversarial Imitation Learning
Tian Xu, Zexuan Chen, Zhilong Zhang +4
Adversarial imitation learning (AIL) achieves high-quality imitation compared to behavioral cloning (BC), but demands substantial online environment interaction. Recent empirical w…
Continual Quadruped Robots Coordination via Semantic Skill Discovery
Daoqing Wang, Yuchen Xiao, Weixuan Huang +5
Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptability to challenging tasks. Exis…
Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
Lixuan Jin, Bingxuan Lan, Xinyi Bao +9
Unmanned aerial vehicles (UAVs) are increasingly being deployed in logistics, service robotics, and other real-world applications, creating a growing demand for autonomous payload…
Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning
Zihao Wang, Shijie Peng, Kerui Wu +6
Humans exhibit remarkable motor agility, enabling a wide range of dynamic skills such as running and jumping, which highlights the great potential of humanoid robots for athletic l…
Anticipation-VLA: Solving Long-Horizon Embodied Tasks via Anticipation-based Subgoal Generation
Zhilong Zhang, Wenyu Luo, Haonan Wang +9
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for embodied intelligence, enabling robots to perform tasks based on natural language instructions and curre…
Model-Based Proactive Cost Generation for Learning Safe Policies Offline with Limited Violation Data
Ruiqi Xue, Lei Yuan, Kainuo Cheng +2
Learning constraint-satisfying policies from offline data without risky online interaction is crucial for safety-critical decision making. Conventional methods typically learn cost…
Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation
Zhichao Wu, Junyin Ye, Zhilong Zhang +6
While current embodied policies exhibit remarkable manipulation skills, their execution remains unsatisfactorily slow as they inherit the tardy pacing of human demonstrations. Exis…
MARVL: Multi-Stage Guidance for Robotic Manipulation via Vision-Language Models
Xunlan Zhou, Xuanlin Chen, Shaowei Zhang +4
Designing dense reward functions is pivotal for efficient robotic Reinforcement Learning (RL). However, most dense rewards rely on manual engineering, which fundamentally limits th…