A Robust Control Framework for Human Motion Prediction

Bajcsy, Andrea; Bansal, Somil; Ratner, Ellis; Tomlin, Claire J.; Dragan, Anca D.

doi:10.1109/lra.2020.3028049

Cited by 20 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As pointed out in ref. [4], intent inference may include the predicted goal of a moving vehicle or a human sharing the workspace, within a prescribed time interval. The prediction dynamically takes into account the change in expected behaviour if the goal or nature of motion changes with time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intent-Aware Optimal Collision Avoidance and Trajectory Planning for a Pursuit Vehicle

2022

View full text Add to dashboard Cite

Summary This paper presents an integrated optimal control framework for velocity and steering control of an autonomous pursuit vehicle, where the control objectives satisfy the requirements of collision avoidance and moving target tracking. A distinctive feature of the proposed velocity and steering control is the application of logarithmic penalty functions to both. The control barrier imposed by logarithmic function provides a unique tool in computing a balanced trajectory with optimal tracking error, control effort and safety margin. Trajectories compliant with the safety regulations for autonomous driving have been planned based on estimated intention of the target and the obstacles. Effects of the controller weights have been extensively simulated to assess the performance of the proposed strategy in a variety of dynamic situations. The controller has been validated on a real-life robot by using a shrinking horizon control policy for iterative optimisation.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Improvements to Bayesian prediction is claimed in ref. [4], which introduces a robust control formulation and ref. [6], which considers mutual-intent decoding and planning between a number of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Intent-Aware Optimal Collision Avoidance and Trajectory Planning for a Pursuit Vehicle

2022

View full text Add to dashboard Cite

show abstract

“…Equipped with our joint dynamics which describe the evolution of the human's state and the learning algorithm, we can now formulate an optimal control problem whose solution captures which human observations "steer" the learning system into desired states. We build on our framework from [23,24] and adapt it for analyzing general discrete-time algorithms that learn online from human data.…”

Section: B Solution Methodsmentioning

confidence: 99%

Analyzing Human Models that Adapt Online

Bajcsy¹,

Siththaranjan²,

Tomlin³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Predictive human models often need to adapt their parameters online from human data. This raises previously ignored safety-related questions for robots relying on these models such as what the model could learn online and how quickly could it learn it. For instance, when will the robot have a confident estimate in a nearby human's goal? Or, what parameter initializations guarantee that the robot can learn the human's preferences in a finite number of observations? To answer such analysis questions, our key idea is to model the robot's learning algorithm as a dynamical system where the state is the current model parameter estimate and the control is the human data the robot observes. This enables us to leverage tools from reachability analysis and optimal control to compute the set of hypotheses the robot could learn in finite time, as well as the worst and best-case time it takes to learn them. We demonstrate the utility of our analysis tool in four human-robot domains, including autonomous driving and indoor navigation.

show abstract

“…Perceived safety is a key aspect to obtain comfortable and socially accepted Human-Robot Interaction (HRI) [5], where robot features (e.g, embodiment, gaze, speech) and abilities adhere to social norms. Human-aware motion planners [6]- [8] have been studied and demonstrated to make users feel more safe around robots [9], [10]. Larger and faster robots have been shown to lower comfort levels in humans [11].…”

Section: B Related Workmentioning

confidence: 99%