8 accepted papers
We present FORGE, a method for sim-to-real transfer of force-aware manipulation policies in the presence of significant pose uncertainty. During simulation-based policy learning, FORGE combines a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">force
Domain randomization in reinforcement learning is an established technique for increasing the robustness of control policies learned in simulation. By randomizing properties of the environment during training, the learned policy can be robust to uncertainty along the randomized dimensions. While the…
In robotic applications such as bin-picking or block-stacking, learned predictive models have been developed for manipulation of objects with varying but known dynamic properties (e.g., mass distributions and friction coefficients). When a robot encounters a new object, these properties are often di…
Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences; and when a plan is found; lower level motion plans are generated. Such a strategy hinges on the ability to reliably predict that a…
Exploration in novel settings can be challenging without prior experience in similar domains. However, humans are able to build on prior experience quickly and efficiently. Children exhibit this behavior when playing with toys. For example, given a toy with a yellow and blue door, a child will explo…
Recovering an unknown reward function for complex manipulation tasks is the fundamental problem of Inverse Reinforcement Learning (IRL). Often, the recovered reward function fails to explicitly capture implicit constraints (e.g., axis alignment, force, or relative alignment) between the manipulator,
Consider a task such as pouring liquid from a cup into a container. Some parameters, such as the location of the pour, are crucial to task success, while others, such as the length of the pour, can exhibit larger variation. In this work, we propose a method that differentiates between specified task
Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality (Liu et al., 2016). Yet having an accurate automatic evalu…