Before We Act: Learning Action-Conditioned Team-Response Beliefs for Decentralized Multi-Robot Manipulation
cs.RO
Submitted: 2026-10-10
Updated: 2026-10-10
Project page: https://jeong-zju.github.io/care
Sources
- CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy
- RoCo: Dialectic Multi-Robot Collaboration with Large Language Models
- TRACE: Trajectory-Routed Causal Memory for Delayed-Evidence Visuomotor Imitation
- RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Robots that Collaborate: Sequential Asymmetric Imitation for Learning Coupled Robot Policies
- Tri-Manual Visuomotor Imitation Learning of Robot Policies
- NestDex: Nested Policy Learning with Copilot Assisted Teleoperation for Dexterous Manipulation
- A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
- Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge
- Emerging Properties in Self-Supervised Vision Transformers
- Constraining Streaming Flow Models for Adapting Learned Robot Trajectory Distributions
- DOSE3 : Diffusion-based Out-of-distribution detection on SE(3) trajectories
- PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies