Search Results for author:
Found 26 papers, 10 papers with code
GePA*SE: Generalized Edge-Based Parallel A* for Slow Evaluations
However, in a number of robotics domains, the action space is heterogenous in the computational effort required to evaluate the cost of an action and its outcome.
A-ePA*SE: Anytime Edge-Based Parallel A* for Slow Evaluations
Anytime search algorithms are useful for planning problems where a solution is desired under a limited time budget.
Multi-objective Conflict-based Search Using Safe-interval Path Planning
This paper addresses a generalization of the well known multi-agent path finding (MAPF) problem that optimizes multiple conflicting objectives simultaneously such as travel time and path risk.
Enhanced Multi-Objective A* Using Balanced Binary Search Trees
This work addresses a Multi-Objective Shortest Path Problem (MO-SPP) on a graph where the goal is to find a set of Pareto-optimal solutions from a start node to a destination in the graph.
AMRA*: Anytime Multi-Resolution Multi-Heuristic A*
A fine discretisation allows for better approximations of the continuous search space, but makes the search for a solution more computationally costly.
Driving in Dense Traffic with Model-Free Reinforcement Learning
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
Planning and Execution using Inaccurate Models with Provable Guarantees
In this paper, we propose CMAX an approach for interleaving planning and execution.
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models
In this paper we propose CMAX++, an approach that leverages real-world experience to improve the quality of resulting plans over successive repetitions of a robotic task.
Interleaving Graph Search and Trajectory Optimization for Aggressive Quadrotor Flight
We introduce a framework for aggressive quadrotor trajectory generation with global reasoning capabilities that combines the best of trajectory optimization and discrete graph search.
Robotics
PERCH: Perception via Search for Multi-Object Recognition and Localization
In many robotic domains such as flexible automated manufacturing or personal assistance, a fundamental perception task is that of identifying and localizing objects whose 3D models are known.
A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction
We evaluate our framework in simulation as well as the real world.
Multi-Resolution A*
We show that MRA* is bounded suboptimal with respect to the anchor resolution search space and resolution complete.
PERCH 2.0 : Fast and Accurate GPU-based Perception via Search for Object Pose Estimation
We show that our approach can achieve a speedup of 100x over PERCH, as well as better accuracy than the state-of-the-art data-driven approaches on 6-DoF pose estimation without the need for annotating ground truth poses in the training data.
Reactive Long Horizon Task Execution via Visual Skill and Precondition Models
Zero-shot execution of unseen robotic tasks is important to allowing robots to perform a wide variety of tasks in human environments, but collecting the amounts of data necessary to train end-to-end policies in the real-world is often infeasible.
Manipulation Planning Among Movable Obstacles Using Physics-Based Adaptive Motion Primitives
Unfortunately, it is infeasible to query the simulator for thousands of actions that need to be evaluated in a typical planning problem as each simulation is time-consuming.
Robotics
Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs
In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action.
Multi-Objective Path-Based D* Lite
Incremental graph search algorithms such as D* Lite reuse previous, and perhaps partial, searches to expedite subsequent path planning tasks.
On the Effectiveness of Iterative Learning Control
However, there is little prior theoretical work that explains the effectiveness of ILC even in the presence of large modeling errors, where optimal control methods using the misspecified model (MM) often perform poorly.
Effective Integration of Weighted Cost-to-go and Conflict Heuristic within Suboptimal CBS
In this paper, we show that, contrary to prevailing CBS beliefs, a weighted cost-to-go heuristic can be used effectively alongside the conflict heuristic in two possible variants.
Non-Blocking Batch A* (Technical Report)
We show how this subtle but important change can lead to substantial reductions in expansions compared to the current blocking alternative, and see that the performance is related to the information difference between the batch computed NN and fast non-NN heuristic.
Efficient Recovery Learning using Model Predictive Meta-Reasoning
We use our approach to learn recovery skills for door-opening and evaluate them both in simulation and on a real robot with little fine-tuning.
Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How
In this paper, we extend M4M and present Enhanced-M4M (E-M4M) -- a systematic graph search-based solver that searches over orderings of pushes for movable objects that need to be rearranged and different possible rearrangements of the scene.
Planning for Complex Non-prehensile Manipulation Among Movable Objects by Interleaving Multi-Agent Pathfinding and Physics-Based Simulation
In particular, our motivation is to allow the robot to reason over and consider non-prehensile rearrangement actions that lead to complex robot-object and object-object interactions where multiple objects might be moved by the robot simultaneously, and objects might tilt, lean on each other, or topple.
Constant-time Motion Planning with Anytime Refinement for Manipulation
However, robotic systems often have more time allotted for planning than the online portion of CTMP requires, time that can be used to improve the solution.
Improving Learnt Local MAPF Policies with Heuristic Search
Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations.
Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences
An exciting frontier in robotic manipulation is the use of multiple arms at once.
