Environment-aware Interactive Movement Primitives for Object Reaching in
Clutter
- URL: http://arxiv.org/abs/2210.16194v1
- Date: Fri, 28 Oct 2022 15:03:23 GMT
- Title: Environment-aware Interactive Movement Primitives for Object Reaching in
Clutter
- Authors: Sariah Mghames, Marc Hanheide
- Abstract summary: We propose a constrained multi-objective optimization framework (OptI-ProMP) to approach the problem of reaching a target in a compact clutter.
OptI-ProMP features costs related to both static, dynamic and pushable objects in the target neighborhood, and it relies on probabilistic primitives for problem initialisation.
We tested, in a simulated poly-tunnel, both ProMP-based planners from literature and the OptI-ProMP, on low (3-dofs) and high (7-dofs) dexterity robot body.
- Score: 4.5459332718995205
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The majority of motion planning strategies developed over the literature for
reaching an object in clutter are applied to two dimensional (2-d) space where
the state space of the environment is constrained in one direction. Fewer works
have been investigated to reach a target in 3-d cluttered space, and when so,
they have limited performance when applied to complex cases. In this work, we
propose a constrained multi-objective optimization framework (OptI-ProMP) to
approach the problem of reaching a target in a compact clutter with a case
study on soft fruits grown in clusters, leveraging the local optimisation-based
planner CHOMP. OptI-ProMP features costs related to both static, dynamic and
pushable objects in the target neighborhood, and it relies on probabilistic
primitives for problem initialisation. We tested, in a simulated poly-tunnel,
both ProMP-based planners from literature and the OptI-ProMP, on low (3-dofs)
and high (7-dofs) dexterity robot body, respectively. Results show collision
and pushing costs minimisation with 7-dofs robot kinematics, in addition to
successful static obstacles avoidance and systematic drifting from the pushable
objects center of mass.
Related papers
- Search-Based Path Planning among Movable Obstacles [8.023424148846265]
This paper introduces two PAMO formulations, i.e., bi-objective and resource constrained problems in an occupancy grid.
We develop PAMO*, a search method with completeness and solution optimality guarantees, to solve the two problems.
We then extend PAMO* to hybrid-state PAMO* to plan in continuous spaces with high-fidelity interaction between the robot and the objects.
arXiv Detail & Related papers (2024-10-24T00:02:58Z) - Dynamic Neural Potential Field: Online Trajectory Optimization in Presence of Moving Obstacles [40.8414230686474]
We address a task of local trajectory planning for the mobile robot in the presence of static and dynamic obstacles.
We develop an approach, where repulsive potential is estimated by the neural model.
We deploy our approach on Husky UGV mobile platform, which move through the office corridors under proposed MPC local trajectory planner.
arXiv Detail & Related papers (2024-10-09T12:27:09Z) - A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem.
We propose a general and open-source framework for modeling and benchmarking TAMP problems.
We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
arXiv Detail & Related papers (2024-08-11T14:57:57Z) - GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered
Environments [2.982218441172364]
This study presents the GP-MPPI, an online learning-based control strategy that integrates Model Predictive Path Intergal (MPPI) with a local perception model.
We validate the efficiency and robustness of our proposed control strategy through both simulated and real-world experiments of 2D autonomous navigation tasks.
arXiv Detail & Related papers (2023-07-08T17:33:20Z) - Planning for Complex Non-prehensile Manipulation Among Movable Objects
by Interleaving Multi-Agent Pathfinding and Physics-Based Simulation [23.62057790524675]
Real-world manipulation problems in heavy clutter require robots to reason about potential contacts with objects in the environment.
We focus on pick-and-place style tasks to retrieve a target object from a shelf where some movable' objects must be rearranged in order to solve the task.
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.
arXiv Detail & Related papers (2023-03-23T15:29:27Z) - Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation [68.56443382421878]
We propose a novel adaptive multi-source predictor for zero-shot video object segmentation (ZVOS)
In the static object predictor, the RGB source is converted to depth and static saliency sources, simultaneously.
Experiments show that the proposed model outperforms the state-of-the-art methods on three challenging ZVOS benchmarks.
arXiv Detail & Related papers (2023-03-18T10:19:29Z) - Self-Supervised Interactive Object Segmentation Through a
Singulation-and-Grasping Approach [9.029861710944704]
We propose a robot learning approach to interact with novel objects and collect each object's training label.
The Singulation-and-Grasping (SaG) policy is trained through end-to-end reinforcement learning.
Our system achieves 70% singulation success rate in simulated cluttered scenes.
arXiv Detail & Related papers (2022-07-19T15:01:36Z) - Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value
Functions [65.84090965167535]
We present Neural Motion Fields, a novel object representation which encodes both object point clouds and the relative task trajectories as an implicit value function parameterized by a neural network.
This object-centric representation models a continuous distribution over the SE(3) space and allows us to perform grasping reactively by leveraging sampling-based MPC to optimize this value function.
arXiv Detail & Related papers (2022-06-29T18:47:05Z) - Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform.
We produce a closed-loop controller to reactively push objects in a continuous action space.
We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
arXiv Detail & Related papers (2021-11-15T18:50:04Z) - POMP: Pomcp-based Online Motion Planning for active visual search in
indoor environments [89.43830036483901]
We focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup.
Our POMP method uses as input the current pose of an agent and a RGB-D frame.
We validate our method on the publicly available AVD benchmark, achieving an average success rate of 0.76 with an average path length of 17.1.
arXiv Detail & Related papers (2020-09-17T08:23:50Z) - Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image.
Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space.
We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step.
This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
arXiv Detail & Related papers (2020-08-31T17:10:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.