11 accepted papers
While robotic manipulation capabilities have advanced rapidly, physical safety remains a major barrier to deploying household robots: task success is insufficient if the robot damages itself or its surroundings. Simulation offers a harm-free alternative to costly and dangerous real-world training an…
Service robots must retrieve objects in dynamic, open-world settings where requests may reference attributes (“the red mug”), spatial context (“the mug on the table”), or past states (“the mug that was here yesterday”). Existing approaches capture only parts of this problem: scene graphs capture spa…
Structural causal models describe how the components of a robotic system interact. They provide both structural and functional information about the relationships that are present in the system. The structural information outlines the variables among which there is interaction. The functional inform
Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured terrains. The effectiveness of these methods hinges on two essential elements: (1) the use of massively parallel physics simulations…
Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the ‘default policy,’ based on previous experience. However, the inherent rigidity of the static default policy presents significant challenges for agents when op…
Robot data collected in complex real-world scenarios are often biased due to safety concerns, human preferences, and mission or platform constraints. Consequently, robot learning from such observational data poses great challenges for accurate parameter estimation. We propose a principled causal inf…
Biological agents, such as humans and animals, are capable of making decisions out of a very large number of choices in a limited time. They can do so because they use their prior knowledge to find a solution that is not necessarily optimal but good enough for the given task. In this work, we study…
We propose a principled kernel-based policy iteration algorithm to solve the continuous-state Markov Decision Processes (MDPs). In contrast to most decision-theoretic planning frameworks, which assume fully known state transition models, we design a method that eliminates such a strong assumption wh…
We investigate the autonomous navigation of a mobile robot in the presence of other moving vehicles under time-varying uncertain environmental disturbances. We first predict the future state distributions of other vehicles to account for their uncertain behaviors affected by the time-varying disturb…
Motion planning under uncertainty for an autonomous system can be formulated as a Markov Decision Process with a continuous state space. In this letter, we propose a novel solution to this decision-theoretic planning problem that directly obtains the continuous value function with only the first and
We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address the decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments. We explore the time variability property of the planning stochasticity and investi…