Programming robots to perform complex tasks is often difficult and time consuming, requiring expert knowledge and skills in robot software and sometimes hardware. Imitation learning is a method for training robots to perform tasks by leveraging human expertise through demonstrations. Typically, the assumption is that those demonstrations are performed by a single, highly competent expert. However, in many real-world applications that use user demonstrations for tasks or incorporate both user data and pretrained data, such as home robotics including assistive robots, this is unlikely to be the case. This paper presents research towards a system which can leverage suboptimal demonstrations to solve ambiguous tasks; and particularly learn from its own failures. This is a negative-feedback system which achieves significant improvement over purely positive imitation learning for ambiguous tasks, achieving a 90% improvement in success rate against a system that does not utilise negative feedback, compared to a 50% improvement in success rate when utilised on a real robot, as well as demonstrating higher efficacy, memory efficiency and time efficiency than a comparable negative feedback scheme. The novel scheme presented in this paper is validated through simulated and real-robot experiments.
Figures & tables
Fig. 1: An illustration of the ambiguity problem in a task avoiding an obstacle while navigating to a goal. The two blue lines represent demonstrations, both successes (neither collide with the obstacle), but the central green line, learned from a combination of the two, is a failure, as it averages the two behaviours and collides with the obstacle.
Fig. 2: An illustration of the mask for an obstacle avoidance task, with zeroes, regions where Nthreshold or greater trajectories pass through, shown in white and ones, where fewer than Nthreshold trajectoreis pass through, shown in blue, and the obstacle being avoided shown in red.
Fig. 3: An illustration of the simple ambiguous task tested here, with the two success modes demonstrated, over (left) and under (right)
Fig. 4: The Negative Feedback graph for the simple ambiguous task, showing the improvement in success rate with increasing cycles of negative feedback, comparing three different methods for applying negative feedback - a negative weighting method, a mixture of experts method, and finally the product of experts method presented in this paper
Fig. 5: The graph showing the impact of different methods of trajectory selection on the same five-cycle run of negative feedback described in 4 . Here, ’Mask 75%’ and ’Mask 50%’ refer to the impacts of using an Nthreshold of 75% of the trajectories and 50% of the trajectories respectively.
Fig. 6: The ’slalom’ variant of the task, which involves navigating around a more complex course, shown in the left image. The right image shows the gaps which the route can traverse, labeled from A to E and from A to D. Any combination of the first row and second row is a viable route through the slalom.
Fig. 7: The Negative Feedback graph for the slalom task, showing the success rate for the three different methods increasing with the number of feedback cycles.
Fig. 8: The Negative Feedback graph for four different masking methods on the slalom task. In this instance, only central trajectory and three different masking distributions - 25%, 50%, and 75% - were considered.
Fig. 9: An illustration of the same pick-and-place with obstacle task as used in the real robot experiments, with the Franka Emika Research 3 arm visible. The highlighted lines illustrate three possible expert behaviours, and then the combination of these, which due to the ambiguity of choice between the three behaviours results in a collision.
Fig. 10: The Negative Feedback graph for the real robot, showing the success rate per 10 runs with an increasing number of negative feedback cycles for the same three methods used in the Simulated experiments.
Fig. 11: The Negative Feedback graph for the efficiency comparison, showing the time taken to complete each step of negative feedback under the Product of Experts and Negative weighting methods, each step averaged over twenty runs.