8 accepted papers
We present TreeIRL, a novel planner for autonomous driving that combines Monte Carlo tree search (MCTS) and inverse reinforcement learning (IRL) to achieve state-of-the-art performance in simulation and in real-world driving. The key idea is to use MCTS to find a promising set of safe candidate traj…
We propose a novel 3-D human motion and object interaction prediction model that is aware of commonsense knowledge about human–object interaction. We jointly predict human joint motion and human–object interactions. The two prediction results are combined to enforce commonsense knowledge, such as "i…
In this paper, we introduce the first published planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set of trajectory proposals and scores them with a learned model. The best trajectory is tracked by our self-driving v…
Aircraft routing problem is a crucial component for flight automation. Despite recent successes, challenges still remain when the environment is dynamic and uncertain. In this paper, we tackle the following two challenges. First, when the environment is uncertain, it is much safer if the route plann…
Humans perceive and describe their surroundings with qualitative statements (e.g., "Alice's hand is in contact with a bottle."), rather than quantitative values (e.g., 6-D poses of Alice's hand and a bottle). Qualitative spatial representation (QSR) is a framework that represents the spatial informa…
Human activity recognition is a crucial ingredient in safe and efficient human-robot collaboration. In this paper, we present a new model-based human activity recognition system called logical activity recognition system (LCARS). LCARS requires much less training data compared to learning-based work…
This paper presents a robust motion planning methodology for autonomous tracked vehicles navigating in a rough and unknown environment. Two fields of study are dealt with in this paper: motion planning and slip estimation. For the motion planner, the CC-RRT* algorithm is combined with LQG-MP. The mo…
This paper presents an optimal global planner for autonomous tracked vehicles navigating in off-road terrain with uncertain slip, which affects the vehicle as a process noise. This paper incorporates two fields of study: slip estimation and motion planning. For slip estimation, an experimental resul