1 citations · 1 across the 6 of their papers we have counts for
6 papers
VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments
Ning Liu, Sen Shen, Zheng Li +5
Hierarchical multi-robot exploration decouples frontier allocation from local navigation, making systems brittle in dynamic environments. Without awareness of execution difficulty,…
CARE: Consistent Action-Response Execution for Human-Guided Robot Transport
Ning Liu, Sen Shen, Zheng Li +5
Human-guided collaborative transport becomes interaction-critical when the operator can continue along a route segment that is not executable by the robot--payload system. In this…
RESPOND: Risk-Enhanced Structured Pattern for LLM-driven Online Node-level Decision-making
Dan Chen, Heye Huang, Tiantian Chen +4
Current LLM-based driving agents that rely on unstructured plain-text memory suffer from low-precision scene retrieval and inefficient reflection. To address this limitation, we pr…
FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks
Haizhou Ge, Yufei Jia, Zheng Li +6
Many contact-rich manipulation tasks require precise force regulation. However, most imitation learning (IL) policies remain position-centric and lack explicit force awareness, and…
ManiVID-3D: Generalizable View-Invariant Reinforcement Learning for Robotic Manipulation via Disentangled 3D Representations
Zheng Li, Pei Qu, Yufei Jia +6
Deploying visual reinforcement learning (RL) policies in real-world manipulation is often hindered by camera viewpoint changes. A policy trained from a fixed front-facing camera ma…
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems
Mingcong Lei, Honghao Cai, Yuyuan Yang +16
Embodied intelligence aims to enable robots to learn, reason, and generalize robustly across complex real-world environments. However, existing approaches often struggle with parti…