81 citations · 96 across the 9 of their papers we have counts for
13 papers
Illumination-Aware Active Perception for Spacecraft Inspection
Sagarika Rao Valluri, Benjamin Riviere
Harsh and rapidly varying orbital illumination introduces failure modes during spacecraft inspection. Existing planners rely on line-of-sight inspection metrics which perform occlu…
Value-Preserving Action Quantization
Max Muchen Sun, Jueun Kwon, Benjamin Riviere +1
Search-based policy learning, such as Monte Carlo tree search (MCTS), offers the flexibility to trade runtime computation for data efficiency and adaptation to novel environments.…
A Unified Kalman-Type Classification of Controllability for Linear Stochastic Systems
Qi Lü, Yu Wang
We provide a unified algebraic classification of approximate controllability, approximate null controllability, exact null controllability, and exact controllability for finite-dim…
World-Action Models for Robot Learning and Control: A Survey
Zuxing Lu, Hongjia Zhai, Guanzhi Wang +13
Robots operating in open environments act under partial observability, physical constraints, and dynamic task contexts. Beyond mapping observations and language instructions to act…
Spectral Decomposition of Inverse Dynamics for Fast Exploration in Model-Based Manipulation
Solvin Sigurdson, Benjamin Riviere, Joel Burdick
Planning long duration robotic manipulation sequences is challenging because of the complexity of exploring feasible trajectories through nonlinear contact dynamics and many contac…
Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems
Benjamin Riviere, John Lathrop, Soon-Jo Chung
The ability of a robot to plan complex behaviors with real-time computation, rather than adhering to predesigned or offline-learned routines, alleviates the need for specialized al…
Model Predictive Trees: Sample-Efficient Receding Horizon Planning with Reusable Tree Search
John Lathrop, Benjamin Rivi`ere, Jedidiah Alindogan +1
We present Model Predictive Trees (MPT), a receding horizon tree search algorithm that improves its performance by reusing information efficiently. Whereas existing solvers reuse o…
Joint-Space Multi-Robot Motion Planning with Learned Decentralized Heuristics
Fengze Xie, Marcus Dominguez-Kuhne, Benjamin Riviere +4
In this paper, we present a method of multi-robot motion planning by biasing centralized, sampling-based tree search with decentralized, data-driven steer and distance heuristics.…
CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems
Hiroyasu Tsukamoto, Benjamin Rivière, Changrak Choi +2
The key innovation of our analytical method, CaRT, lies in establishing a new hierarchical, distributed architecture to guarantee the safety and robustness of a given learning-base…
H-TD2: Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatch
Benjamin Rivière, Soon-Jo Chung
We present H-TD2: Hybrid Temporal Difference Learning for Taxi Dispatch, a model-free, adaptive decision-making algorithm to coordinate a large fleet of automated taxis in a dynami…
Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments
Benjamin Riviere, Wolfgang Hoenig, Matthew Anderson +1
We present a self-improving, Neural Tree Expansion (NTE) method for multi-robot online planning in non-cooperative environments, where each robot attempts to maximize its cumulativ…
Distributed Adaptive Reinforcement Learning: A Method for Optimal Routing
Salar Rahili, Benjamin Riviere, Soon-Jo Chung
In this paper, a learning-based optimal transportation algorithm for autonomous taxis and ridesharing vehicles is presented. The goal is to design a mechanism to solve the routing…
GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
Benjamin Rivière, Wolfgang Hoenig, Yisong Yue +1
We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage o…