5 papers
MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning
Yusuf Syed, Viraj Parimi, Brian Williams
Temporally contrastive representation learning induces a latent structure capable of reducing long-horizon planning to inference in a low-dimensional linear system. However, existi…
Large Neighborhood Search for Multi-Agent Task Assignment and Path Finding with Precedence Constraints
Viraj Parimi, Brian C. Williams
Many multi-robot applications require tasks to be completed efficiently and in the correct order, so that downstream operations can proceed at the right time. Multi-agent path find…
Risk-Bounded Multi-Agent Visual Navigation via Iterative Risk Allocation
Viraj Parimi, Brian C. Williams
Safe navigation is essential for autonomous systems operating in hazardous environments, especially when multiple agents must coordinate using only high-dimensional visual observat…
Diffusion-Guided Multi-Arm Motion Planning
Viraj Parimi, Brian C. Williams
Multi-arm motion planning is fundamental for enabling arms to complete complex long-horizon tasks in shared spaces efficiently but current methods struggle with scalability due to…
Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning
Meng Feng, Viraj Parimi, Brian Williams
Safe navigation is essential for autonomous systems operating in hazardous environments. Traditional planning methods excel at long-horizon tasks but rely on a predefined graph wit…