Towards Probabilistic Planning of Explanations for Robot Navigation
- URL: http://arxiv.org/abs/2411.05022v1
- Date: Sat, 26 Oct 2024 09:52:14 GMT
- Title: Towards Probabilistic Planning of Explanations for Robot Navigation
- Authors: Amar Halilovic, Senka Krivic,
- Abstract summary: This paper introduces a novel approach that integrates user-centered design principles directly into the core of robot path planning processes.
We propose a probabilistic framework for automated planning of explanations for robot navigation.
- Score: 2.6196780831364643
- License:
- Abstract: In robotics, ensuring that autonomous systems are comprehensible and accountable to users is essential for effective human-robot interaction. This paper introduces a novel approach that integrates user-centered design principles directly into the core of robot path planning processes. We propose a probabilistic framework for automated planning of explanations for robot navigation, where the preferences of different users regarding explanations are probabilistically modeled to tailor the stochasticity of the real-world human-robot interaction and the communication of decisions of the robot and its actions towards humans. This approach aims to enhance the transparency of robot path planning and adapt to diverse user explanation needs by anticipating the types of explanations that will satisfy individual users.
Related papers
- $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge.
We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
arXiv Detail & Related papers (2024-10-31T17:22:30Z) - Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction [52.12746368727368]
Differentiable simulation has become a powerful tool for system identification.
Our approach calibrates object properties by using information from the robot, without relying on data from the object itself.
We demonstrate the effectiveness of our method on a low-cost robotic platform.
arXiv Detail & Related papers (2024-10-04T20:48:38Z) - A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation [39.87346821309096]
We present an addressee estimation model with improved performance in comparison with the previous SOTA.
We also propose several ways to incorporate explainability and transparency in the aforementioned architecture.
arXiv Detail & Related papers (2024-05-20T13:09:32Z) - QUAR-VLA: Vision-Language-Action Model for Quadruped Robots [37.952398683031895]
The central idea is to elevate the overall intelligence of the robot.
We propose QUAdruped Robotic Transformer (QUART), a family of VLA models to integrate visual information and instructions from diverse modalities as input.
Our approach leads to performant robotic policies and enables QUART to obtain a range of emergent capabilities.
arXiv Detail & Related papers (2023-12-22T06:15:03Z) - Automated Process Planning Based on a Semantic Capability Model and SMT [50.76251195257306]
In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function.
We present an approach that combines these two topics: starting from a semantic capability model, an AI planning problem is automatically generated.
arXiv Detail & Related papers (2023-12-14T10:37:34Z) - Towards a Causal Probabilistic Framework for Prediction,
Action-Selection & Explanations for Robot Block-Stacking Tasks [4.244706520140677]
Causal models provide a principled framework to encode formal knowledge of the causal relationships that govern the robot's interaction with its environment.
We propose a novel causal probabilistic framework to embed a physics simulation capability into a structural causal model to permit robots to perceive and assess the current state of a block-stacking task.
arXiv Detail & Related papers (2023-08-11T15:58:15Z) - Understanding a Robot's Guiding Ethical Principles via Automatically
Generated Explanations [4.393037165265444]
We build upon an existing ethical framework to allow users to make suggestions about plans and receive automatically generated contrastive explanations.
Results of a user study indicate that the generated explanations help humans to understand the ethical principles that underlie a robot's plan.
arXiv Detail & Related papers (2022-06-20T22:55:00Z) - Synthesis and Execution of Communicative Robotic Movements with
Generative Adversarial Networks [59.098560311521034]
We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects.
We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics.
We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
arXiv Detail & Related papers (2022-03-29T15:03:05Z) - Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and
Robotics Together [68.44697646919515]
This paper presents several human-robot systems that utilize spatial computing to enable novel robot use cases.
The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning.
arXiv Detail & Related papers (2022-02-03T10:04:26Z) - 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) - Autonomous Planning Based on Spatial Concepts to Tidy Up Home
Environments with Service Robots [5.739787445246959]
We propose a novel planning method that can efficiently estimate the order and positions of the objects to be tidied up by learning the parameters of a probabilistic generative model.
The model allows a robot to learn the distributions of the co-occurrence probability of the objects and places to tidy up using the multimodal sensor information collected in a tidied environment.
We evaluate the effectiveness of the proposed method by an experimental simulation that reproduces the conditions of the Tidy Up Here task of the World Robot Summit 2018 international robotics competition.
arXiv Detail & Related papers (2020-02-10T11:49:58Z)
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.