9 citations · 9 across the 13 of their papers we have counts for
12 papers · 1 filter
Learning Scene-Aware Humanoid Locomotion through 3D Clutter from Immersive Human Demonstrations
Beichen Wang, Tong Xu, Daniel Kosukhin +3
While learning from human motions has enabled highly dynamic humanoid skills such as dancing and martial arts in obstacle-free space, traversal through densely cluttered environmen…
CAVE-NAV: VLM-Based Autonomous 3D Navigation in Underwater Cave Environments
Zhenqi Wu, Yuanjie Lu, Yisheng Zhang +4
Autonomous navigation in underwater cave environments is essential for search-and-rescue operations, scientific exploration, and emergency egress. Traditional navigation systems co…
CORAL: COntextual Reasoning And Local Planning in A Hierarchical VLM Framework for Underwater Monitoring
Zhenqi Wu, Yuanjie Lu, Xuesu Xiao +1
Oyster reefs are critical ecosystem species that sustain biodiversity, filter water, and protect coastlines, yet they continue to decline globally. Restoring these ecosystems requi…
Moving Through Clutter: Scaling Data Collection and Benchmarking for 3D Scene-Aware Humanoid Locomotion via Virtual Reality
Beichen Wang, Yuanjie Lu, Linji Wang +2
Recent advances in humanoid locomotion have enabled dynamic behaviors such as dancing, martial arts, and parkour, yet these capabilities are predominantly demonstrated in open, fla…
APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model
Yuanjie Lu, Beichen Wang, Zhengqi Wu +4
Autonomous navigation in highly constrained environments remains challenging for mobile robots. Classical navigation approaches offer safety assurances but require environment-spec…
Adaptive Dynamics Planning for Robot Navigation
Yuanjie Lu, Mingyang Mao, Tong Xu +3
Autonomous robot navigation systems often rely on hierarchical planning, where global planners compute collision-free paths without considering dynamics, and local planners enforce…
Combining Machine Learning and Sampling-Based Search for Multi-Goal Motion Planning with Dynamics
Yuanjie Lu, Erion Plaku
This paper considers multi-goal motion planning in unstructured, obstacle-rich environments where a robot is required to reach multiple regions while avoiding collisions. The plann…
Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning
Linji Wang, Tong Xu, Yuanjie Lu +1
Robotics Reinforcement Learning (RL) often relies on carefully engineered auxiliary rewards to supplement sparse primary learning objectives to compensate for the lack of large-sca…
Decremental Dynamics Planning for Robot Navigation
Yuanjie Lu, Tong Xu, Linji Wang +2
Most, if not all, robot navigation systems employ a decomposed planning framework that includes global and local planning. To trade-off onboard computation and plan quality, curren…
Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for Vertically Challenging Terrain
Tong Xu, Chenhui Pan, Madhan B. Rao +4
Recent advancement in off-road autonomy has shown promises in deploying autonomous mobile robots in outdoor off-road environments. Encouraging results have been reported from both…
Multi-Goal Motion Memory
Yuanjie Lu, Dibyendu Das, Erion Plaku +1
Autonomous mobile robots (e.g., warehouse logistics robots) often need to traverse complex, obstacle-rich, and changing environments to reach multiple fixed goals (e.g., warehouse…
Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning
Dibyendu Das, Yuanjie Lu, Erion Plaku +1
When facing a new motion-planning problem, most motion planners solve it from scratch, e.g., via sampling and exploration or starting optimization from a straight-line path. Howeve…