11 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…
This paper proposes an algorithm capable of driving a system to follow a piecewise linear trajectory without prior knowledge of the system dynamics. Motivated by a critical failure scenario in which a system can experience an abrupt change in its dynamics, we demonstrate that it is possible to follo…
This paper addresses the challenge of autonomous excavation of challenging terrains, in particular those that are prone to jamming and inter-particle adhesion when tackled by a standard penetrate-drag-scoop motion pattern. Inspired by human excavation strategies, our approach incorporates oscillator
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…
We study <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">capacity- and budget-constrained multi-agent MDPs</i> (CB-MA-MDPs), a class that captures many maintenance and scheduling tasks in which each agent can irreversibly fail and a planner must deci
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…
Autonomous lander missions on extraterrestrial bodies will need to sample granular material while coping with domain shift, no matter how well a sampling strategy is tuned on Earth. This paper proposes an adaptive scooping strategy that uses deep Gaussian process method trained with meta-learning to…
Autonomously selecting the right sequence of locations to sample is critical during exploration missions in unknown environments, with constraints on the number of samples that can be collected, and a possibility of system failure. A key idea for decision-making in unknown environments is to exploit…