452 research outputs found
Parallel Monte Carlo Tree Search with Batched Rigid-body Simulations for Speeding up Long-Horizon Episodic Robot Planning
We propose a novel Parallel Monte Carlo tree search with Batched Simulations
(PMBS) algorithm for accelerating long-horizon, episodic robotic planning
tasks. Monte Carlo tree search (MCTS) is an effective heuristic search
algorithm for solving episodic decision-making problems whose underlying search
spaces are expansive. Leveraging a GPU-based large-scale simulator, PMBS
introduces massive parallelism into MCTS for solving planning tasks through the
batched execution of a large number of concurrent simulations, which allows for
more efficient and accurate evaluations of the expected cost-to-go over large
action spaces. When applied to the challenging manipulation tasks of object
retrieval from clutter, PMBS achieves a speedup of over with an
improved solution quality, in comparison to a serial MCTS implementation. We
show that PMBS can be directly applied to real robot hardware with negligible
sim-to-real differences. Supplementary material, including video, can be found
at https://github.com/arc-l/pmbs.Comment: Accepted for IROS 202
Probabilistic Slide-support Manipulation Planning in Clutter
To safely and efficiently extract an object from the clutter, this paper
presents a bimanual manipulation planner in which one hand of the robot is used
to slide the target object out of the clutter while the other hand is used to
support the surrounding objects to prevent the clutter from collapsing. Our
method uses a neural network to predict the physical phenomena of the clutter
when the target object is moved. We generate the most efficient action based on
the Monte Carlo tree search.The grasping and sliding actions are planned to
minimize the number of motion sequences to pick the target object. In addition,
the object to be supported is determined to minimize the position change of
surrounding objects. Experiments with a real bimanual robot confirmed that the
robot could retrieve the target object, reducing the total number of motion
sequences and improving safety.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS 2023) (Accepted
Learning deep policies for physics-based robotic manipulation in cluttered real-world environments
This thesis presents a series of planners and learning algorithms for real-world manipulation in clutter. The focus is on interleaving real-world execution with look-ahead planning in simulation as an effective way to address the uncertainty arising from complex physics interactions and occlusions.
We introduce VisualRHP, a receding horizon planner in the image space guided by a learned heuristic. VisualRHP generates, in closed-loop, prehensile and non-prehensile manipulation actions to manipulate a desired object in clutter while avoiding dropping obstacle objects off the edge of the manipulation surface. To acquire the heuristic of VisualRHP, we develop deep imitation learning and deep reinforcement learning algorithms specifically tailored for environments with complex dynamics and requiring long-term sequential decision making. The learned heuristic ensures generalization over different environment settings and transferability of manipulation skills to different desired objects in the real world.
In the second part of this thesis, we integrate VisualRHP with a learnable object pose estimator to guide the search for an occluded desired object. This hybrid approach harnesses neural networks with convolution and recurrent structures to capture relevant information from the history of partial observation to guide VisualRHP future actions.
We run an ablation study over the different component of VisualRHP and compare it with model-free and model-based alternatives. We run experiments in different simulation environments and real-world settings. The results show that by trading a small computation time for heuristic-guided look-ahead planning, VisualRHP delivers a more robust and efficient behaviour compared to alternative state-of-the-art approaches while still operating in near real-time
Lazy Rearrangement Planning in Confined Spaces
Object rearrangement is important for many applications but remains
challenging, especially in confined spaces, such as shelves, where objects
cannot be accessed from above and they block reachability to each other. Such
constraints require many motion planning and collision checking calls, which
are computationally expensive. In addition, the arrangement space grows
exponentially with the number of objects. To address these issues, this work
introduces a lazy evaluation framework with a local monotone solver and a
global planner. Monotone instances are those that can be solved by moving each
object at most once. A key insight is that reachability constraints at the
grasps for objects' starts and goals can quickly reveal dependencies between
objects without having to execute expensive motion planning queries. Given
that, the local solver builds lazily a search tree that respects these
reachability constraints without verifying that the arm paths are collision
free. It only collision checks when a promising solution is found. If a
monotone solution is not found, the non-monotone planner loads the lazy search
tree and explores ways to move objects to intermediate locations from where
monotone solutions to the goal can be found. Results show that the proposed
framework can solve difficult instances in confined spaces with up to 16
objects, which state-of-the-art methods fail to solve. It also solves problems
faster than alternatives, when the alternatives find a solution. It also
achieves high-quality solutions, i.e., only 1.8 additional actions on average
are needed for non-monotone instances.Comment: Accepted to the 32nd International Conference on Automated Planning
and Scheduling (ICAPS 2022
Prioritized Planning for Target-Oriented Manipulation via Hierarchical Stacking Relationship Prediction
In scenarios involving the grasping of multiple targets, the learning of
stacking relationships between objects is fundamental for robots to execute
safely and efficiently. However, current methods lack subdivision for the
hierarchy of stacking relationship types. In scenes where objects are mostly
stacked in an orderly manner, they are incapable of performing human-like and
high-efficient grasping decisions. This paper proposes a perception-planning
method to distinguish different stacking types between objects and generate
prioritized manipulation order decisions based on given target designations. We
utilize a Hierarchical Stacking Relationship Network (HSRN) to discriminate the
hierarchy of stacking and generate a refined Stacking Relationship Tree (SRT)
for relationship description. Considering that objects with high stacking
stability can be grasped together if necessary, we introduce an elaborate
decision-making planner based on the Partially Observable Markov Decision
Process (POMDP), which leverages observations and generates the least
grasp-consuming decision chain with robustness and is suitable for
simultaneously specifying multiple targets. To verify our work, we set the
scene to the dining table and augment the REGRAD dataset with a set of common
tableware models for network training. Experiments show that our method
effectively generates grasping decisions that conform to human requirements,
and improves the implementation efficiency compared with existing methods on
the basis of guaranteeing the success rate.Comment: 8 pages, 8 figure
Efficient Belief Propagation for Perception and Manipulation in Clutter
Autonomous service robots are required to perform tasks in common human indoor environments. To achieve goals associated with these tasks, the robot should continually perceive, reason its environment, and plan to manipulate objects, which we term as goal-directed manipulation. Perception remains the most challenging aspect of all stages, as common indoor environments typically pose problems in recognizing objects under inherent occlusions with physical interactions among themselves. Despite recent progress in the field of robot perception, accommodating perceptual uncertainty due to partial observations remains challenging and needs to be addressed to achieve the desired autonomy.
In this dissertation, we address the problem of perception under uncertainty for robot manipulation in cluttered environments using generative inference methods. Specifically, we aim to enable robots to perceive partially observable environments by maintaining an approximate probability distribution as a belief over possible scene hypotheses. This belief representation captures uncertainty resulting from inter-object occlusions and physical interactions, which are inherently present in clutterred indoor environments. The research efforts presented in this thesis are towards developing appropriate state representations and inference techniques to generate and maintain such belief over contextually plausible scene states. We focus on providing the following features to generative inference while addressing the challenges due to occlusions: 1) generating and maintaining plausible scene hypotheses, 2) reducing the inference search space that typically grows exponentially with respect to the number of objects in a scene, 3) preserving scene hypotheses over continual observations.
To generate and maintain plausible scene hypotheses, we propose physics informed scene estimation methods that combine a Newtonian physics engine within a particle based generative inference framework. The proposed variants of our method with and without a Monte Carlo step showed promising results on generating and maintaining plausible hypotheses under complete occlusions. We show that estimating such scenarios would not be possible by the commonly adopted 3D registration methods without the notion of a physical context that our method provides.
To scale up the context informed inference to accommodate a larger number of objects, we describe a factorization of scene state into object and object-parts to perform collaborative particle-based inference. This resulted in the Pull Message Passing for Nonparametric Belief Propagation (PMPNBP) algorithm that caters to the demands of the high-dimensional multimodal nature of cluttered scenes while being computationally tractable. We demonstrate that PMPNBP is orders of magnitude faster than the state-of-the-art Nonparametric Belief Propagation method. Additionally, we show that PMPNBP successfully estimates poses of articulated objects under various simulated occlusion scenarios.
To extend our PMPNBP algorithm for tracking object states over continuous observations, we explore ways to propose and preserve hypotheses effectively over time. This resulted in an augmentation-selection method, where hypotheses are drawn from various proposals followed by the selection of a subset using PMPNBP that explained the current state of the objects. We discuss and analyze our augmentation-selection method with its counterparts in belief propagation literature. Furthermore, we develop an inference pipeline for pose estimation and tracking of articulated objects in clutter. In this pipeline, the message passing module with the augmentation-selection method is informed by segmentation heatmaps from a trained neural network. In our experiments, we show that our proposed pipeline can effectively maintain belief and track articulated objects over a sequence of observations under occlusion.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163159/1/kdesingh_1.pd
- …