58 citations · 276 across the 26 of their papers we have counts for
10 papers · 1 filter
The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms
Alexander Koenig, Zixi Liu, Lucas Janson +1
A long-standing question in robot hand design is how accurate tactile sensing must be. This paper uses simulated tactile signals and the reinforcement learning (RL) framework to st…
Map-Predictive Motion Planning in Unknown Environments
Amine Elhafsi, Boris Ivanovic, Lucas Janson +1
Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods…
Revisiting the Asymptotic Optimality of RRT
Kiril Solovey, Lucas Janson, Edward Schmerling +2
RRT* is one of the most widely used sampling-based algorithms for asymptotically-optimal motion planning. This algorithm laid the foundations for optimality in motion planning as a…
Safe Motion Planning in Unknown Environments: Optimality Benchmarks and Tractable Policies
Lucas Janson, Tommy Hu, Marco Pavone
This paper addresses the problem of planning a safe (i.e., collision-free) trajectory from an initial state to a goal region when the obstacle space is a-priori unknown and is incr…
An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning
Joseph A. Starek, Javier V. Gomez, Edward Schmerling +3
Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merg…
Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty
Lucas Janson, Edward Schmerling, Marco Pavone
This article presents a novel approach, named MCMP (Monte Carlo Motion Planning), to the problem of motion planning under uncertainty, i.e., to the problem of computing a low-cost…
Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance
Lucas Janson, Brian Ichter, Marco Pavone
Probabilistic sampling-based algorithms, such as the probabilistic roadmap (PRM) and the rapidly-exploring random tree (RRT) algorithms, represent one of the most successful approa…
Optimal Sampling-Based Motion Planning under Differential Constraints: the Drift Case with Linear Affine Dynamics
Edward Schmerling, Lucas Janson, Marco Pavone
In this paper we provide a thorough, rigorous theoretical framework to assess optimality guarantees of sampling-based algorithms for drift control systems: systems that, loosely sp…
Optimal Sampling-Based Motion Planning under Differential Constraints: the Driftless Case
Edward Schmerling, Lucas Janson, Marco Pavone
Motion planning under differential constraints is a classic problem in robotics. To date, the state of the art is represented by sampling-based techniques, with the Rapidly-explori…
Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions
Lucas Janson, Edward Schmerling, Ashley Clark +1
In this paper we present a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT*). The algorithm is specifically aimed at solvi…