A Single Chip System for Sensor Data Fusion Based on a Drift-diffusion Model

Current multisensory system face data communication overhead in integrating disparate sensor data to build a coherent and accurate global phenomenon. We present here a novel hardware and software co-design platform for a heterogeneous data fusion solution based on a perceptual decision making approach (the drift-diffusion model). It provides a convenient infrastructure for sensor data acquisition and data integration and only uses a single chip Xilinx ZYNQ-7000 XC7Z020 AP SOC. A case study of controlling the moving speed of a single ground-based robot, according to physiological states of the operator based on heart rates, is conducted and demonstrates the possibility of integrated sensor data fusion architecture. The results of our DDM-based data integration shows a better correlation coefficient with the raw ECG signal compare with a simply piecewise approach

Concurrent Skill Composition using Ensemble of Primitive Skills

One of the key characteristics of an open-ended cumulative learning agent is that it should use the knowledge gained from prior learning to solve future tasks. That characteristic is especially essential in robotics, as learning every perception-action skill from scratch is not only time consuming but may not always be feasible. In the case of reinforcement learning, this learned knowledge is called a policy. The lifelong learning agent should treat the policies of learned tasks as building blocks to solve those future tasks. One of the categorizations of tasks is based on its composition, ranging from primitive tasks to compound tasks that are either a sequential or concurrent combination of primitive tasks. Thus, the agent needs to be able to combine the policies of the primitive tasks to solve compound tasks, which are then added to its knowledge base. Inspired by modular neural networks, we propose an approach to compose policies for compound tasks that are concurrent combinations of disjoint tasks. Furthermore, we hypothesize that learning in a specialized environment leads to more efficient learning; hence, we create scaffolded environments for the robot to learn primitive skills for our mobile robot-based experiments. We then show how the agent can combine those primitive skills to learn solutions for compound tasks. That reduces the overall training time of multiple skills and creates a versatile agent that can mix and match the skills.</p

Optimality and limitations of audio-visual integration for cognitive systems

Multimodal integration is an important process in perceptual decision-making. In humans, this process has often been shown to be statistically optimal, or near optimal: sensory information is combined in a fashion that minimizes the average error in perceptual representation of stimuli. However, sometimes there are costs that come with the optimization, manifesting as illusory percepts. We review audio-visual facilitations and illusions that are products of multisensory integration, and the computational models that account for these phenomena. In particular, the same optimal computational model can lead to illusory percepts, and we suggest that more studies should be needed to detect and mitigate these illusions, as artifacts in artificial cognitive systems. We provide cautionary considerations when designing artificial cognitive systems with the view of avoiding such artifacts. Finally, we suggest avenues of research toward solutions to potential pitfalls in system design. We conclude that detailed understanding of multisensory integration and the mechanisms behind audio-visual illusions can benefit the design of artificial cognitive systems.Human-Robot Interactio

Beyond reach: Do symmetric changes in motor costs affect decision making?:A registered report

Executing an important decision can be as easy as moving a mouse cursor or reaching towards the preferred option with a hand. But would we decide differently if choosing required walking a few steps towards an option? More generally, is our preference invariant to the means and motor costs of reporting it? Previous research demonstrated that asymmetric motor costs can nudge the decision-maker towards a less costly option. However, virtually all traditional decision-making theories predict that increasing motor costs symmetrically for all options should not affect choice in any way. This prediction is disputed by the theory of embodied cognition, which suggests that motor behavior is an integral part of cognitive processes, and that motor costs can affect our choices. In this registered report, we investigated whether varying motor costs can affect response dynamics and the final choices in an intertemporal choice task: choosing between a readily available small reward and a larger but delayed reward. Our study compared choices reported by moving a computer mouse cursor towards the preferred option with the choices executed via a more motor costly walking procedure. First, we investigated whether relative values of the intertemporal choice options affect walking trajectories in the same way as they affect mouse cursor dynamics. Second, we tested a hypothesis that, in the walking condition, increased motor costs of a preference reversal would decrease the number of changes-of-mind and therefore increase the proportion of impulsive, smaller-but-sooner choices. We confirmed the hypothesis that walking trajectories reflect covert dynamics of decision making, and rejected the hypothesis that increased motor costs of responding affect decisions in an intertemporal choice task. Overall, this study contributes to the empirical basis enabling the decision-making theories to address the complex interplay between cognitive and motor processes

Experience-Based Generation of Maintenance and Achievement Goals on a Mobile Robot

Learning skills or knowledge online from experiences is attractive for robots because it permits them to develop new behavior autonomously. However, the onus lies with the system designer to specify which skills or knowledge the robot should learn. Experience-based goal generation algorithms permit a robot to decide autonomously what it will to learn. This paper presents an adaptive resonance theory approach to experience-based generation of approach, avoidance, maintenance and achievement goals for a mobile robot. An experimental analysis is conducted to explore the relationship between algorithm parameters and goals generated on a simulated ePuck robot. Results show how parameter choice influences the number, stability and nature of generated goals. We identify theweight representations, distance functions and update rules that are appropriate for a mobile robot to generate maintenance and achievement goals

Modelling human arm motion through the attractor dynamics approach

Konferenzbericht "2013 IEEE International Conference on Robotics and Biomimetics

A Review of the Relationship between Novelty, Intrinsic Motivation and Reinforcement Learning

This paper presents a review on the tri-partite relationship between novelty, intrinsic motivation and reinforcement learning. The paper first presents a literature survey on novelty and the different computational models of novelty detection, with a specific focus on the features of stimuli that trigger a Hedonic value for generating a novelty signal. It then presents an overview of intrinsic motivation and investigations into different models with the aim of exploring deeper co-relationships between specific features of a novelty signal and its effect on intrinsic motivation in producing a reward function. Finally, it presents survey results on reinforcement learning, different models and their functional relationship with intrinsic motivation