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This paper revisits the stochastic computing paradigm as a way to implement architectures dedicated to probabilistic inference. In general, it is assumed the operation over stochastic bit streams is robust with respect to radiation transient events effects. Moreover, it can be expected that leveraging the stochastic computing paradigm to implement probabilistic computations such as Bayesian inference implemented in hardware could yield an increased resilience to radiation effects comparatively to deterministic procedures. However, the practical assessment of the robustness against radiation is mandatory before considering stochastic Bayesian machines (SBMs) in hazardous environments. Results of fault injection campaigns at register transfer level provide the first evidences of the intrinsic robustness of SBMs with respect to single event upsets and single event transients.

A task-level robot system named Handey, which is under development, is described. The current system is limited to pick-and-place operations, and it has successfully carried out dozens of such operations involving a variety of parts in relatively complex environments. The pick-and-place problem is described, and approximate approaches to the problem are examined. Heuristic motion planning in Handey is then discussed. >