Modeling the large space of possible human motions requires scal- able techniques. Generalizing from example motions or exam- ple controllers is one way to provide the required scalability. We present techniques for generalizing a controller for physics-based walking to significantly different tasks, such as climbing a large step up, or pushing a heavy object. Continuation methods solve such problems using a progressive sequence of problems that trace a path from an existing solved problem to the final desired-but- unsolved problem. Each step in the continuation sequence makes progress towards the target problem while further adapting the so- lution. We describe and evaluate a number of choices in applying continuation methods to adapting walking gaits for tasks involving interaction with the environment. The methods have been success- fully applied to automatically adapt a regular cyclic walk to climb- ing a 65cm step, stepping over a 55cm sill, pushing heavy furniture, walking up steep inclines, and walking on ice. The continuation path further provides parameterized solutions to these problems.
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