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Abstract-Carrying heavy payloads is a challenging task for the modular robot, because its composing modules are relatively tiny and less strong compared with conventional robots. To accomplish this task, we attached passive rollers to the modular robot, and designed a wheeled locomotion gait called tricycleBot. The gait is inspired by paddling motion, and is implemented on the modular robot called SuperBot. Features of this gait are systematically studied and verified through extensive experiments. It is shown that tricycleBot can carry payloads at least 530% of its own weight. It can also be steered remotely to move forward/backward, turn left/right. Capability of tricycleBot demonstrates that the versatility of modular robot can be further expanded to solve very specialized and challenging tasks by using heterogeneous devices.

Abstract-Three-dimensional, physics-based simulators are important to the field of self-reconfigurable robotics because they allow researchers to approximate the physical interactions and autonomous behaviors of large numbers of modules in a low-cost, safe, and highly-controlled manner. This paper presents a novel, high-performance, general-purpose simulator for autonomous, self-reconfigurable robots called ReMod3D (RM3D) that overcomes the speed and scalability limitations of existing self-reconfigurable simulators while, at the same time, allowing for realistic module structures, complex environments, and high physical simulation fidelity. While most existing selfreconfigurable simulators view modules as actuated physical bodies with programmable controllers, RM3D views them as embodied agents, defined not only by their physical bodies (links, joints, docks, sensors, actuators) but also by their minds (actions, percepts, behaviors, world models) and the noise inherent in the interaction between sensors, actuators, and the environment. RM3D also simulates inter-module dock connection breakage, something novel for self-reconfigurable robot simulators. Additionally, we present experimental evidence showing that this novel architecture makes RM3D well-suited to locomotion, manipulation, reconfiguration, and embodied intelligence research.

Abstract-Learning from surprises and unexpected situations is a capability that is critical for developmental learning. This paper describes a promising approach in which a learner robot engages in a cyclic learning process consisting of "prediction, action, observation, analysis (of surprise) and adaptation". In particular, the robot always predicts the consequences of its actions, detects surprises whenever there is a significant discrepancy between the prediction and the observed reality, analyzes the surprises for causes, and uses the analyzed knowledge to adapt to the unexpected situations. We tested this approach on a modular robot learning how to navigate and recover from unexpected changes in sensors, actions, goals, and environments. The results are very encouraging.Index Terms-Learning systems, unsupervised learning, surprise-based learning, developmental robotics.