In human-robot collaboration, the objectives of the human are often unknown to the robot. Moreover, even assuming a known objective, the human behavior is also uncertain. In order to plan a robust robot behavior, a key preliminary question is then: How to derive realistic human behaviors given a known objective? A major issue is that such a human behavior should itself account for the robot behavior, otherwise collaboration cannot happen. In this paper, we rely on Markov decision models, representing the uncertainty over the human objective as a probability distribution over a finite set of objective functions (inducing a distribution over human behaviors). Based on this, we propose two contributions: 1) an approach to automatically generate an uncertain human behavior (a policy) for each given objective function while accounting for possible robot behaviors; and 2) a robot planning algorithm that is robust to the above-mentioned uncertainties and relies on solving a partially observable Markov decision process (POMDP) obtained by reasoning on a distribution over human behaviors. A co-working scenario allows conducting experiments and presenting qualitative and quantitative results to evaluate our approach.

Decentralized partially observable Markov decision processes (Dec-POMDPs) formalize the problem of designing individual controllers for a group of collaborative agents under stochastic dynamics and partial observability. Seeking a global optimum is difficult (NEXP complete), but seeking a Nash equilibrium - each agent policy being a best response to the other agents - is more accessible, and allowed addressing infinite-horizon problems with solutions in the form of finite state controllers. In this paper, we show that this approach can be adapted to cases where only a generative model (a simulator) of the Dec-POMDP is available. This requires relying on a simulation-based POMDP solver to construct an agent’s FSC node by node. A related process is used to heuristically derive initial FSCs. Experiment with benchmarks shows that MC-JESP is competitive with existing Dec-POMDP solvers, even better than many offline methods using explicit models.

This paper looks at solving collaborative planning problems formalized as Decentralized POMDPs (Dec-POMDPs) by searching for Nash equilibria, i.e., situations where each agent’s policy is a best response to the other agents’ (fixed) policies. While the Joint Equilibrium-based Search for Policies (JESP) algorithm does this in the finite-horizon setting relying on policy trees, we propose here to adapt it to infinite-horizon Dec-POMDPs by using finite state controller (FSC) policy representations. In this article, we (1) explain how to turn a Dec-POMDP with N − 1 fixed FSCs into an infinite-horizon POMDP whose solution is an Nthagent best response; (2) propose a JESP variant, called Inf-JESP, using this to solve infinite-horizon Dec-POMDPs; (3) introduce heuristic initializations for JESP aiming at leading to good solutions; and (4) conduct experiments on state-of-the-art benchmark problems to evaluate our approach. This paper looks at solving collaborative planning problems formalized as Decentralized POMDPs (Dec-POMDPs) by searching for Nash equilibria, i.e., situations where each agent’s policy is a best response to the other agents’ (fixed) policies. While the Joint Equilibrium-based Search for Policies (JESP) algorithm does this in the finite-horizon setting relying on policy trees, we propose here to adapt it to infinite-horizon Dec-POMDPs by using finite state controller (FSC) policy representations. In this article, we (1) explain how to turn a Dec-POMDP with N 1 fixed FSCs into an infinite-horizon POMDP whose solution−is an Nthagent best response; (2) propose a JESP variant, called Inf-JESP, using this to solve infinite-horizon Dec-POMDPs; (3) introduce heuristic initializations for JESP aiming at leading to good solutions; and (4) conduct experiments on state-of-the-art benchmark problems to evaluate our approach.

Load-sensing floors are capable of tracking objects without suffering from occlusions nor posing the same privacy issues as cameras. They have been mostly used to analyze human gait as a way of continuous diagnosis but could also be placed alongside robots to help monitoring in specialized institutions, such as elderly care facilities. However, large-scale deployments necessitate cheap sensors which do not necessarily offer the same precision. With more noisy sensors, lighter robots might be difficult to track and precisely localize. In this article, we investigate various models in order to estimate the position of a robot. We experiment with several robots of different weights and compare the models' estimates against ground truth measurements provided by a motion capture system. We show that with standard-sized tiles of 60 cm, we can track even the lighter robots with less than 4 cm of error.

This paper addresses the problem of localizing and tracking one intermittent, moving sound source using a microphone array on a mobile robot. Robot motion provides a solution for estimating the distance to the source and avoiding front-back ambiguity. We propose a mixture Kalman filter (MKF) framework in order to fuse the robot motion information and the measurements taken at different poses of the robot. Experiments and statistical results demonstrate the ability of the proposed method to track one intermittent sound source in a reverberant environment where false measurements of the source angle of arrival (AoA) and the source activity often occur compared to a method that does not consider tracking source activity into account.

New applications of mobile robotics in dynamic urban areas require more than the single-session geometric maps that have dominated simultaneous localization and mapping (SLAM) research to date; maps must be updated as the environment changes and include a semantic layer (such as road network information) to aid motion planning in dynamic environments. We present an algorithm for long-term localization and mapping in real time using a three-dimensional (3D) laser scanner. The system infers the static or dynamic state of each 3D point in the environment based on repeated observations. The velocity of each dynamic point is estimated without requiring object models or explicit clustering of the points. At any time, the system is able to produce a most-likely representation of underlying static scene geometry. By storing the time history of velocities, we can infer the dominant motion patterns within the map. The result is an online mapping and localization system specifically designed to enable long-term autonomy within highly dynamic environments. We validate the approach using data collected around the campus of ETH Zurich over seven months and several kilometers of navigation. To the best of our knowledge, this is the first work to unify long-term map update with tracking of dynamic objects.

One milestone for autonomous mobile robotics is to endow robots with the capability to compute the plans and motor commands necessary to reach a defined goal position. For indoor or car-like robots moving on flat terrain, this problem is well mastered and open-source software can be deployed to such robots. However, for many applications such as search and rescue, ground robots must handle three-dimensional terrain. In this article, we present a system that is able to plan and execute a path in a complex environment starting from noisy sensor input. In order to cope with the complexity of a high-dimensional configuration space, we separate position and configuration planning. We demonstrate our system on a search and rescue robot with flippers by climbing up and down a difficult curved staircase.

In this paper, we present a novel semi-supervised clustering approach based on Markov process. It deals with data which include abundant local constraints. We apply the designed model to a topological region extraction problem, where topological segmentation is constructed based on sparse human inputs (potentially provided by human experts). The model considers human indications as seeds for topological regions, i.e. the partially labeled data. It results in a regional topological segmentation of connected free space.

When people communicate with robots, the most intuitive mean is by naming the different regions in the environment. The capability that robots are able to identify different regions highly depends on the unsupervised topological segmentation results. This paper addresses the problem of segmenting a metric map into regions. Nowadays many researches in this direction develop approaches based on spectral clustering. However there are inherent drawbacks of spectral clustering algorithms. In this paper, we first discuss these drawbacks using several testing results; then we propose our approach based on information theory which uses Chow-Liu tree to segment the composed graph according to the weight differences. The results show that our method provides more flexible and faster results in the sense of facilitating semantic mapping or further applications.

The increasing number of ICP variants leads to an explosion of algorithms and parameters. This renders difficult the selection of the appropriate combination for a given application. In this paper, we propose a state-of-the-art, modular, and efficient implementation of an ICP library. We took advantage of the recent availability of fast depth cameras to demonstrate one application example: a 3D pose tracker running at 30 Hz. For this application, we show the modularity of our ICP library by optimizing the use of lean and simple descriptors in order to ease the matching of 3D point clouds. This tracker is then evaluated using datasets recorded along a ground truth of millimeter accuracy. We provide both source code and datasets to the community in order to accelerate further comparisons in this field.

This paper presents a semi-autonomous navigation strategy aimed at the control of assistive devices (e.g. an intelligent wheelchair) using low throughput interfaces. A mobile robot proposes the most probable action, as analyzed from the environment, to a human user who can either accept or reject the proposition. In case of rejection, the robot will propose another action, until both entities agree on what needs to be done. In a known environment, the system infers the intended goal destination based on the first executed actions. Furthermore, we endowed the system with learning capabilities, so as to learn the user habits depending on contextual information (e.g. time of the day or if a phone rings). This additional knowledge allows the robot to anticipate the user intention and propose appropriate actions, or goal destinations.

More and more fields of applied computer science involve fusion of multiple data sources, such as sensor readings or model decision. However, incompleteness of the model prevents the programmer from having an absolute precision over their variables. Therefore Bayesian framework can be adequate fro such a process as it allows handling of uncertainty. We will be interested in the ability to express any fusion process as a product, for it can lead to reduction of complexity in time and space. We study in this paper various fusion schemes and propose to add consistency variable to justify the use of a product to compute distribution over the fused variable. We will then show application of this new fusion process to localization of a mobile robot and obstacle avoidance.