Dídac Busquets

Socially Distributed Perception: GRACE plays social tag at AAAI 2005

This paper presents a robot search task (social tag) that uses social interaction, in the form of asking for help, as an integral component of task completion. Socially distributed perception is defined as a robots ability to augment its limited sensory capacities through social interaction. We describe the task of social tag and its implementation on the robot GRACE for the AAAI 2005 Mobile Robot Competition & Exhibition. We then discuss our observations and analyses of GRACEs performance as a situated interaction with conference participants. Our results suggest we were successful in promoting a form of social interaction that allowed people to help the robot achieve its goal. Furthermore, we found that different social uses of the physical space had an effect on the nature of the interaction. Finally, we discuss the implications of this design approach for effective and compelling human-robot interaction, considering its relationship to concepts such as dependency, mixed initiative, and socially distributed cognition.

A Multiagent Approach to Qualitative Landmark-Based Navigation

In this paper we present a multiagent system for landmark-based navigation in unknown environments. We propose a bidding mechanism to coordinate the actions requested by the different agents. The navigation system has been tested on a real robot on indoor unstructured environments.

Reinforcement learning for landmark-based robot navigation

In landmark-based navigation, robots start in an unknown locationand must navigate to a desired target using visually-acquired land-marks. In the scenario that we are studying the target isvisible fromthe robot’s initial location, but it may subsequently be occludedby intervening objects. The challenge for the robot is to acquireenough information about the environment so that it can, even inthat case move, from the starting location to the target position.In this paper, we build upon our previously described multiagentsystem for outdoor landmark-based navigation [2]. It is composedof three systems: the Pilot, responsible for all motions of the robot,the Vision system, responsible for identifying and tracking land-marks and for detecting obstacles, and the Navigation system, re-sponsible for choosing high-level robot motions.These three systems must cooperate to achieve the overall task ofreaching the target. For instance, the Pilot needs the Vision systemto identify obstacles and the Navigation system to select a path tothe goal. The systems are also competing, for instance, the Pilotand the Navigation system both compete for the Vision system. ThePilot needs it for obstacle avoidance, while the Navigation systemneeds it for landmark detection and tracking.To manage this cooperation and competition, in [2] we had chosena bidding mechanism. Each system generates bids for the servicesoffered by the Pilot and Vision systems. The service actually exe-cuted by each system depends on the winning bid at each point intime. In [2] we proposed bidding functions to obtain good perfor-mance from the combined system. In this paper we use Reinforce-ment Learning (RL) [5] to tune the parameters of those functions.The Navigation system is also implemented as a multiagent sys-tem composed of six agents with the following goals: keep the tar-get located with maximum precision and reach it (Target Tracker),keep the risk of losing the target low (Risk Manager), recover from

Multiagent Bidding Mechanisms for Robot Qualitative Navigation

This paper explores the use of bidding mechanisms to coordinate the actions requested by a group of agents in charge of achieving the task of guiding a robot towards a specified target in an unknown environment. This approach is based on a qualitative (fuzzy) approach to landmark-based navigation.

Evolving a multiagent system for landmark-based robot navigation

In this article, we build upon a multiagent architecture for landmark‐based navigation in unknown environments. In this architecture, each of the agents in the navigation system has a bidding function that is controlled by a set of parameters. We show here the good results obtained by an evolutionary approach that tunes the parameter set values for two navigation tasks.

Socially distributed perception

This paper presents a robot search task (social tag) that uses social interaction, in the form of asking for help, as an integral component of task completion. We define socially distributed perception as a robots ability to augment its limited sensory capacities through social interaction.

Learning when to Auction and when to Bid

The market based approach is widely used to solve the problem of multirobot coordination. In this approach, communication and computation costs are key issues, but have not been carefully addressed by the different architectures in the literature. In this paper, we present a method to reduce these costs, by adding the capability to learn whether a task is worth offering up for auction and also whether it is worth bidding for the task, based on previous experience about successful and unsuccessful bids. We show that the method significantly decreases communication and computation costs, while maintaining good overall performance of the team.

A Multi-agent Architecture Integrating Learning and Fuzzy Techniques for Landmark-Based Robot Navigation

This paper extends a navigation system implemented as a multi-agent system (MAS). The arbitration mechanism controlling the interactions between the agents was based on manually-tuned bidding functions. A difficulty with hand-tuning is that it is hard to handle situations involving complex tradeoffs. In this paper we explore the suitability of reinforcement learning for automatically tuning agents within a MAS to optimize a complex tradeoff, namely the camera use.