Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation

• URL: http://arxiv.org/abs/2402.02499v1
• Date: Sun, 4 Feb 2024 14:18:20 GMT
• Title: Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation
• Authors: Pinhao Song, Pengteng Li, Erwin Aertbelien, Renaud Detry
• Abstract summary: We develop a novel intent estimator dubbed the emphRobot Trajectron (RT) RT produces a probabilistic representation of the robot's anticipated trajectory based on its recent position, velocity and acceleration history. We derive a novel shared-control solution that combines RT's predictive capacity to a representation of the locations of potential reaching targets.
• Score: 2.273531916003657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of (a) predicting the trajectory of an arm reaching motion, based on a few seconds of the motion's onset, and (b) leveraging this predictor to facilitate shared-control manipulation tasks, easing the cognitive load of the operator by assisting them in their anticipated direction of motion. Our novel intent estimator, dubbed the \emph{Robot Trajectron} (RT), produces a probabilistic representation of the robot's anticipated trajectory based on its recent position, velocity and acceleration history. Taking arm dynamics into account allows RT to capture the operator's intent better than other SOTA models that only use the arm's position, making it particularly well-suited to assist in tasks where the operator's intent is susceptible to change. We derive a novel shared-control solution that combines RT's predictive capacity to a representation of the locations of potential reaching targets. Our experiments demonstrate RT's effectiveness in both intent estimation and shared-control tasks. We will make the code and data supporting our experiments publicly available at https://github.com/mousecpn/Robot-Trajectron.git.

Related papers

• Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation [65.46610405509338]
  We seek to learn a generalizable goal-conditioned policy that enables zero-shot robot manipulation. Our framework,Track2Act predicts tracks of how points in an image should move in future time-steps based on a goal. We show that this approach of combining scalably learned track prediction with a residual policy enables diverse generalizable robot manipulation.
  arXiv  Detail & Related papers  (2024-05-02T17:56:55Z)
• Towards Agile Robots: Intuitive Robot Position Speculation with Neural Networks [4.193801074793624]
  This paper proposes a robot position speculation network (RPSN), a learning-based approach to enhance the agility of mobile manipulators. The RPSN incorporates a differentiable inverse kinematic algorithm and a neural network. Through end-to-end training, the RPSN can speculate positions with a high success rate.
  arXiv  Detail & Related papers  (2024-02-26T03:54:32Z)
• LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [80.86089324742024]
  We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem. CNN processes localized perception; a graph neural network (GNN) facilitates robot communications. Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
  arXiv  Detail & Related papers  (2024-01-10T00:08:00Z)
• ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting [8.274511768083665]
  We present ManiCast, a novel framework that learns cost-aware human forecasts and feeds them to a model predictive control planner. Our framework enables fluid, real-time interactions between a human and a 7-DoF robot arm across a number of real-world tasks.
  arXiv  Detail & Related papers  (2023-10-20T03:34:31Z)
• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• Deep Functional Predictive Control for Strawberry Cluster Manipulation using Tactile Prediction [6.365634303789478]
  This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed.
  arXiv  Detail & Related papers  (2023-03-09T16:31:35Z)
• Reconstructing Robot Operations via Radio-Frequency Side-Channel [1.0742675209112622]
  In recent years, a variety of attacks have been proposed that actively target the robot itself from the cyber domain. In this work, we investigate whether an insider adversary can accurately fingerprint robot movements and operational warehousing via the radio frequency side channel.
  arXiv  Detail & Related papers  (2022-09-21T08:14:51Z)
• 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)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Projection Mapping Implementation: Enabling Direct Externalization of Perception Results and Action Intent to Improve Robot Explainability [62.03014078810652]
  Existing research on non-verbal cues, e.g., eye gaze or arm movement, may not accurately present a robot's internal states. Projecting the states directly onto a robot's operating environment has the advantages of being direct, accurate, and more salient.
  arXiv  Detail & Related papers  (2020-10-05T18:16:20Z)
• Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space [13.64392246529041]
  Concept of programming by demonstration or imitation learning is used for trajectory planning of manipulators mounted on small spacecraft. For greater autonomy in future space missions and minimal human intervention through ground control, a robot arm having 7-Degrees of Freedom (DoF) is envisaged for carrying out multiple tasks like debris removal, on-orbit servicing and assembly.
  arXiv  Detail & Related papers  (2020-08-10T10:18:04Z)

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.