6 accepted papers
Diffusion-based trajectory planners can synthesize rich, multimodal robot motions from demonstrations, but their iterative denoising makes online planning and control prohibitively slow. Existing accelerations either modify the sampler or compress the network–sacrificing plan quality or requiring re…
Robots equipped with rich sensor suites can localize reliably in partially-observable environments---but powering every sensor continuously is wasteful and often infeasible. Belief-space planners address this by propagating pose-belief covariance through analytic models and switching sensors heurist…
Autonomous motion planning under unknown nonlinear dynamics presents significant challenges. An agent needs to continuously explore the system dynamics to acquire its properties, such as reachability, in order to guide system navigation adaptively. In this paper, we propose a hybrid planning-control
Predicting the near-term behavior of a reactive agent is crucial in many robotic scenarios, yet remains challenging when observations of that agent are sparse or intermittent. Vision-Language Models (VLMs) offer a promising avenue by integrating textual domain knowledge with visual cues, but their o
Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates—the process by which the agent obtains its true state information from the sensors—are limited, presents a significant challenge. Traditional methods often
Optimal decision-making presents a significant challenge for autonomous systems operating in uncertain, stochastic and time-varying environments. Environmental variability over time can significantly impact the system’s optimal decision making strategy for mission completion. To model such environme…