1 citations · 1 across the 5 of their papers we have counts for
6 papers
Planner-Conditioned Diffusion for Coordinated Multi-Agent Exploration
Marcus Yu Siong Teo, Jeric Lew, Tanishq Duhan +1
Coordinated multi-agent exploration requires not only efficient individual coverage but also non-redundant coverage across agents over extended planning horizons. Conventional appr…
FARE: Fast-Slow Agentic Robotic Exploration
Shuhao Liao, Xuxin Lv, Jeric Lew +6
This work advances autonomous robot exploration by integrating agent-level semantic reasoning with fast local control. We introduce FARE, a hierarchical autonomous exploration fram…
AID: Agent Intent from Diffusion for Multi-Agent Informative Path Planning
Jeric Lew, Yuhong Cao, Derek Ming Siang Tan +1
Information gathering in large-scale or time-critical scenarios (e.g., environmental monitoring, search and rescue) requires broad coverage within limited time budgets, motivating…
HELM: Human-Preferred Exploration with Language Models
Shuhao Liao, Xuxin Lv, Yuhong Cao +3
In autonomous exploration tasks, robots are required to explore and map unknown environments while efficiently planning in dynamic and uncertain conditions. Given the significant v…
SALON: Self-supervised Adaptive Learning for Off-road Navigation
Matthew Sivaprakasam, Samuel Triest, Cherie Ho +5
Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse sc…
DARE: Diffusion Policy for Autonomous Robot Exploration
Yuhong Cao, Jeric Lew, Jingsong Liang +2
Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the curren…