Rehabilitative motor training is currently one of the most widely used approaches to promote moderate recovery following injuries of the central nervous system. Such training is generally applied in the clinical setting, whereas it is not standard in preclinical research. This is a concern as it is becoming increasingly apparent that neuroplasticity enhancing treatments require training or some form of activity as a co-therapy to promote functional recovery. Despite the importance of training and the many open questions regarding its mechanistic consequences, its use in preclinical animal models is rather limited. Here we review approaches, findings and challenges when training is applied in animal models of spinal cord injury, and we suggest recommendations to facilitate the integration of training using an appropriate study design, into pre-clinical studies.

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Spartacus is our robot entry in the 2005 AAAI Mobile Robot Challenge, making a robot attend the National Conference on Artificial Intelligence. Designing robots that are capable of interacting with humans in real-life settings can be considered the ultimate challenge when it comes to intelligent autonomous systems. One key issue is the integration of multiple modalities (e.g., mobility, physical structure, navigation, vision, audition, dialogue, reasoning). Such integration increases the diversity and also the complexity of interactions the robot can generate. It also makes it difficult to monitor how such increased capabilities are used in unconstrained conditions, whether it is done while the robot is in operation of afterwards. This paper reports solutions and findings resulting from our hardware, software and decisional integration work on Spartacus. It also outlines perspectives in making intelligent and interaction capabilities evolve for autonomous robots.

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In real world planning problems, it might not be possible for an automated agent to satisfy all the objectives assigned to it. When this situation arises, classical planning returns no plan. In partial satisfaction planning, it is possible to satisfy only a subset of the objectives. To solve this kind of problems, an agent can select a subset of objectives and return the plan that maximizes the net benefit, i.e. the sum of satisfied objectives utilities minus the sum of the cost of actions. This approach has been experimented for deterministic planning. This paper extends partial satisfaction planning for problems with uncertainty on time. For problems under uncertainty, the best subset of objectives can not be calculated at planning time. The effective duration of actions at execution time may dynamically influence the achievable subset of objectives. Our approach introduces special abort actions to explicitly abort objectives. These actions can have deadlines in order to control when objectives can be aborted.