Author name cluster

Martin Levihn

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

IROS Conference 2016 Conference Paper

Navigation Among Movable Obstacles with learned dynamic constraints

Jonathan Scholz
Nehchal Jindal
Martin Levihn
Charles Isbell
Henrik I. Christensen

In this paper we present the first planner for the problem of Navigation Among Movable Obstacles (NAMO) on a real robot that can handle environments with under-specified object dynamics. This result makes use of recent progress from two threads of the Reinforcement Learning literature. The first is a hierarchical Markov-Decision Process formulation of the NAMO problem designed to handle dynamics uncertainty. The second is a physics-based Reinforcement Learning framework which offers a way to ground this uncertainty in a compact model space that can be efficiently updated from data received by the robot online. Our results demonstrate the ability of a robot to adapt to unexpected object behavior in a real office scenario.

Details

ICRA Conference 2015 Conference Paper

Learning non-holonomic object models for mobile manipulation

Jonathan Scholz
Martin Levihn
Charles Isbell
Henrik I. Christensen
Mike Stilman

For a mobile manipulator to interact with large everyday objects, such as office tables, it is often important to have dynamic models of these objects. However, as it is infeasible to provide the robot with models for every possible object it may encounter, it is desirable that the robot can identify common object models autonomously. Existing methods for addressing this challenge are limited by being either purely kinematic, or inefficient due to a lack of physical structure. In this paper, we present a physics-based method for estimating the dynamics of common non-holonomic objects using a mobile manipulator, and demonstrate its efficiency compared to existing approaches.

Details

ICML Conference 2014 Conference Paper

A Physics-Based Model Prior for Object-Oriented MDPs

Jonathan Scholz
Martin Levihn
Charles Isbell
David Wingate

One of the key challenges in using reinforcement learning in robotics is the need for models that capture natural world structure. There are, methods that formalize multi-object dynamics using relational representations, but these methods are not sufficiently compact for real-world robotics. We present a physics-based approach that exploits modern simulation tools to efficiently parameterize physical dynamics. Our results show that this representation can result in much faster learning, by virtue of its strong but appropriate inductive bias in physical environments.

Details

ICRA Conference 2014 Conference Paper

Autonomous environment manipulation to assist humanoid locomotion

Martin Levihn
Koichi Nishiwaki
Satoshi Kagami
Mike Stilman

Legged robots have unique capabilities to traverse complex environments by stepping over and onto objects. Many footstep planners have been developed to take advantage of these capabilities. However, legged robots also have inherent constraints such as a maximum step height and distance. These constraints typically limit their reachable space, independent of footstep planning. Thus, we propose that robots such as humanoid robots that have manipulation capabilities should use them. A robot should autonomously modify its environment if necessary. We present a system that enabled a real robot to use a box to create itself a stair step or place a board on the ground to cross a gap, allowing it to reach its otherwise unreachable goal configuration.

Details

IROS Conference 2014 Conference Paper

Using environment objects as tools: Unconventional door opening

Martin Levihn
Mike Stilman

Robots should be able to utilize environment objects as tools. A critical challenge to accomplishing this task is the vast search space that arises when considering multiple interacting bodies. To manage this complexity, we introduce an approach which efficiently reasons by back-propagating physical constraints between useful combinations of objects. This approach allows us to exploit restrictions on relative object configurations to reduce the search space prior to committing to specific object choices. We present a simulated implementation of our approach applied to the problem of opening a jammed door. Our method allows a robot to efficiently choose between two strategies, leverage and impact, to achieve the desired result using various available objects.

Details

IROS Conference 2013 Conference Paper

Foresight and reconsideration in hierarchical planning and execution

Martin Levihn
Leslie Pack Kaelbling
Tomás Lozano-Pérez
Mike Stilman

We present a hierarchical planning and execution architecture that maintains the computational efficiencyofhierar-chical decomposition while improving optimality. It provides mechanisms for monitoring the belief state during execution and performing selective replanningtorepair poor choices and take advan-tageofnew opportunities. It also provides mechanisms for looking ahead into future planstoavoid making short-sighted choices. The effectivenessofthis architectureis shown through comparative experimentsinsimulation and demonstratedonareal PR2 robot.

Details

ICRA Conference 2013 Conference Paper

Planning with movable obstacles in continuous environments with uncertain dynamics

Martin Levihn
Jonathan Scholz
Mike Stilman

In this paper we present a decision theoretic planner for the problem of Navigation Among Movable Obstacles (NAMO) operating under conditions faced by real robotic systems. While planners for the NAMO domain exist, they typically assume a deterministic environment or rely on discretization of the configuration and action spaces, preventing their use in practice. In contrast, we propose a planner that operates in real-world conditions such as uncertainty about the parameters of workspace objects and continuous configuration and action (control) spaces. To achieve robust NAMO planning despite these conditions, we introduce a novel integration of Monte Carlo simulation with an abstract MDP construction. We present theoretical and empirical arguments for time complexity linear in the number of obstacles as well as a detailed implementation and examples from a dynamic simulation environment.

Details

RLDM Conference 2013 Conference Abstract

What Does Physics Bias: A Comparison of Model Priors for Robot Manipulation

Jonathan Scholz
Martin Levihn

We explore robot object manipulation as a Bayesian model-based reinforcement learning prob- lem under a collection of different model priors. Our main contribution is to highlight the limitations of classical non-parametric regression approaches in the context of online learning, and to introduce an alter- native approach based on monolithic physical inference. The primary motivation for this line of research is to incorporate physical system identification into the RL model, where it can be integrated with modern approaches to Bayesian structure learning. Overall, our results support the idea that modern physical simu- lation tools provide a model space with an appropriate inductive bias for manipulation problems in natural environments.

PDF Details

IROS Conference 2012 Conference Paper

Multi-robot multi-object rearrangement in assignment space

Martin Levihn
Takeo Igarashi
Mike Stilman

We present Assignment Space Planning, a new efficient robot multi-agent coordination algorithm for the PSPACE-hard problem of multi-robot multi-object push rearrangement. In both simulated and real robot experiments, we demonstrate that our method produces optimal solutions for simple problems and exhibits novel emergent behaviors for complex scenarios. Assignment Space takes advantage of the domain structure by splitting the planning up into three stages, effectively reducing the search space size and enabling the planner to produce optimized plans in seconds. Our algorithm finds solutions of comparable quality to complete configuration space search while reducing the computing time to seconds, which allows our approach to be applied in practical scenarios in real-time.

Details

IROS Conference 2010 Conference Paper

Navigation Among Movable Obstacles in unknown environments

Hai-Ning Wu
Martin Levihn
Mike Stilman

This paper explores the Navigation Among Movable Obstacles (NAMO) problem in an unknown environment. We consider the realistic scenario in which the robot has to navigate to a goal position in an unknown environment consisting of static and movable objects. The robot may move objects if the goal can not be reached otherwise or if moving the object may significantly shorten the path to the goal. We consider real situations in which the robot only has limited sensing information and where the action selection can therefore only be based on partial knowledge learned from the environment at that point. This paper introduces an algorithm that significantly reduces the necessary calculations to accomplish this task compared to a direct approach. We present an efficient implementation for the case of planar, axis-aligned environments and report experimental results on challenging scenarios with more than 50 objects.

Details