Evaluating Continual Learning on a Home Robot

Therefore, we split the action prediction problem into two steps: (1) we predict a Most Relevant Point, or MRP, which tells us which region of the world the policy must attend to; and (2) we reactively predict actions which determine where the robot should move in relation to that MRP: for example, how to approach the handle of an oven and when to close the gripper to grasp it. These two operations are performed sequentially using a modified PointNet++ (Qi et al., 2017) model that we refer to as Attention-based PointNet (A-PointNet), shown in Figure 2. The MRP Predictor can then be agnostic to the position of the robot, instead focusing on the features of the object relevant to the overall task, while the Action Predictor can learn to focus on features relevant just to what the next action should be. For example, in Figure 7, the MRP Predictor learns to focus on the handle; the Action Predictor focuses on the angle of the oven door. Image Pre-Processing First we convert the RGB and depth images into a point cloud. We augment the point cloud of the current timestep with our context c, the point cloud from the beginning of the episode. This aids both in combating occlusion, as well as in disambiguating between similar observations that occur during different trajectories. To reduce compute, we crop the working area to 1m, and down-sample using grid pooling, with a resolution of 1cm for the current timestep and 2.5cm for the context. Specifically, we select a random point in each voxel, to reduce overfitting.