Risk-Biased Trajectory Forecasting for Safe Human-Robot Interaction
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Safe planning is critical for human-robot interaction where robot error can cause injury. Risk measures (e.g., CVaR) promote safety of robot plans, whose evaluation is possible in principle using probabilistic forecasts of human motion. Unfortunately, existing generative forecasting methods are unreliable for risk-evaluation due to inaccurate characterization of uncertainty and finite-sampling, possibly leading to overconfident robot behavior. This paper proposes a novel and deliberate biasing of the forecasting model so that any risk evaluation simplifies to a small finite-sampling and expected cost computation. This benefits online planning where only a small number of samples can be taken due to real-time constraints.
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A follow-up conference submission can be found here.