Counterfactual reasoning about intent for interactive navigation in dynamic environments

Bordallo, Alejandro; Previtali, Fabio; Nardelli, Nantas; Ramamoorthy, Subramanian

doi:10.1109/iros.2015.7353783

Cited by 20 publications

(17 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The intention aware reactive avoidance scheme proposed by [24] uses counterfactual reasoning to calculate probabilities over a possible set of navigational goals. Using such probability set, this approach predicts human motion towards most probable goal and generates locally optimal motions for multiple robots.…”

Section: Planning For the Robot And The Humanmentioning

confidence: 99%

Viewing Robot Navigation in Human Environment as a Cooperative Activity

Khambhaita

Alami

2019

Springer Proceedings in Advanced Robotics

View full text Add to dashboard Cite

We claim that navigation in human environments can be viewed as cooperative activity especially in constrained situations. Humans concurrently aid and comply with each other while moving in a shared space. Cooperation helps pedestrians to efficiently reach their own goals and respect conventions such as the personal space of others. To meet human comparable efficiency, a robot needs to predict the human trajectories and plan its own trajectory correspondingly in the same shared space. In this work, we present a navigation planner that is able to plan such cooperative trajectories, simultaneously enforcing the robot's kinematic constraints and avoiding other non-human dynamic obstacles. Using robust social constraints of projected time to a possible future collision, compatibility of human-robot motion direction, and proxemics, our planner is able to replicate human-like navigation behavior not only in open spaces but also in confined areas. Besides adapting the robot trajectory, the planner is also able to proactively propose co-navigation solutions by jointly computing human and robot trajectories within the same optimization framework. We demonstrate richness and performance of the cooperative planner with simulated and real world experiments on multiple interactive navigation scenarios.

show abstract

Section: Planning For the Robot And The Humanmentioning

confidence: 99%

Viewing Robot Navigation in Human Environment as a Cooperative Activity

Khambhaita

Alami

2019

Springer Proceedings in Advanced Robotics

View full text Add to dashboard Cite

show abstract

“…Using simulations as models Models provide numerous advantages in machine learning [12], enabling inferences from limited data, and in planning [13], enabling counter-factual reasoning [14] and guided search. However, defining the structure of models in a way that leads to efficient inference while maintaining fidelity to complex arrangements of physical causes tends to be non-trivial.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Active Localization of Gas Leaks Using Fluid Simulation

Asenov

Rutkauskas²,

Reid³

et al. 2019

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

Sensors are routinely mounted on robots to acquire various forms of measurements in spatio-temporal fields. Locating features within these fields and reconstruction (mapping) of the dense fields can be challenging in resource-constrained situations, such as when trying to locate the source of a gas leak from a small number of measurements. In such cases, a model of the underlying complex dynamics can be exploited to discover informative paths within the field. We use a fluid simulator as a model, to guide inference for the location of a gas leak. We perform localization via minimization of the discrepancy between observed measurements and gas concentrations predicted by the simulator. Our method is able to account for dynamically varying parameters of wind flow (e.g., direction and strength), and its effects on the observed distribution of gas. We develop algorithms for off-line inference as well as for on-line path discovery via active sensing. We demonstrate the efficiency, accuracy and versatility of our algorithm using experiments with a physical robot conducted in outdoor environments. We deploy an unmanned air vehicle (UAV) mounted with a CO2 sensor to automatically seek out a gas cylinder emitting CO2 via a nozzle. We evaluate the accuracy of our algorithm by measuring the error in the inferred location of the nozzle, based on which we show that our proposed approach is competitive with respect to state of the art baselines.

show abstract

“…The third set of constraints (5) ensures that every node is allocated to exactly one computer -note that since a ROS node cannot assume two different settings at the same time, it is not necessary to add an extra restriction to guarantee that exactly one setting of each node is allocated. The last set of constraints (6) ensures that the overall value for any combination of weights for the nodes is always the same. Finally, we add two new sets of constraints to the model, which specifically apply to distributed ROS systems.…”

Section: Problems Definitionmentioning

confidence: 99%

Automatic configuration of ROS applications for near-optimal performance

Cano

Bordallo

Nagarajan

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

The performance of a ROS application is a function of the individual performance of its constituent nodes. Since ROS nodes are typically configurable (parameterised), the specific parameter values adopted will determine the level of performance generated. In addition, ROS applications may be distributed across multiple computation devices, thus providing different options for node allocation. We address two configuration problems that the typical ROS user is confronted with: i) Determining parameter values and node allocations for maximising performance; ii) Determining node allocations for minimising hardware resources that can guarantee the desired performance. We formalise these problems with a mathematical model, a constrained form of a multiple-choice multiple knapsack problem. We propose a greedy algorithm for optimising each problem, using linear regression for predicting the performance of an individual ROS node over a continuum set of parameter combinations. We evaluate the algorithms through simulation and we validate them in a real ROS scenario, showing that the expected performance levels only deviate from the real measurements by an average of 2.5%.

show abstract

Counterfactual reasoning about intent for interactive navigation in dynamic environments

Cited by 20 publications

References 22 publications

Viewing Robot Navigation in Human Environment as a Cooperative Activity

Viewing Robot Navigation in Human Environment as a Cooperative Activity

Active Localization of Gas Leaks Using Fluid Simulation

Automatic configuration of ROS applications for near-optimal performance

Contact Info

Product

Resources

About