Reactive Planar Manipulation with Convex Hybrid MPC

This paper presents a reactive controller for planar manipulation tasks that leverages machine learning to achieve real-time performance. The approach is based on a Model Predictive Control (MPC) formulation, where the goal is to find an optimal sequence of robot motions to achieve a desired object motion. Due to the multiple contact modes associated with frictional interactions, the resulting optimization program suffers from combinatorial complexity when tasked with determining the optimal sequence of modes. To overcome this difficulty, we formulate the search for the optimal mode sequences offline, separately from the search for optimal control inputs online. Using tools from machine learning, this leads to a convex hybrid MPC program that can be solved in real-time. We validate our algorithm on a planar manipulation experimental setup where results show that the convex hybrid MPC formulation with learned modes achieves good closed-loop performance on a trajectory tracking problem

Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration

We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation. The controller is a recurrent neural network using raw images as input and generating robot arm trajectories, with the parameters shared across the tasks. The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction. Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks

Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence

An important challenge for safety in machine learning and artificial intelligence systems is a~set of related failures involving specification gaming, reward hacking, fragility to distributional shifts, and Goodhart's or Campbell's law. This paper presents additional failure modes for interactions within multi-agent systems that are closely related. These multi-agent failure modes are more complex, more problematic, and less well understood than the single-agent case, and are also already occurring, largely unnoticed. After motivating the discussion with examples from poker-playing artificial intelligence (AI), the paper explains why these failure modes are in some senses unavoidable. Following this, the paper categorizes failure modes, provides definitions, and cites examples for each of the modes: accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking. The paper then discusses how extant literature on multi-agent AI fails to address these failure modes, and identifies work which may be useful for the mitigation of these failure modes.Comment: 12 Pages, This version re-submitted to Big Data and Cognitive Computing, Special Issue "Artificial Superintelligence: Coordination & Strategy

Holistic engineering design : a combined synchronous and asynchronous approach

To aid the creation and through-life support of large, complex engineering products, organizations are placing a greater emphasis on constructing complete and accurate records of design activities. Current documentary approaches are not sufficient to capture activities and decisions in their entirety and can lead to organizations revisiting and in some cases reworking design decisions in order to understand previous design episodes. Design activities are undertaken in a variety of modes; many of which are dichotomous, and thus each require separate documentary mechanisms to capture information in an efficient manner. It is possible to identify the modes of learning and transaction to describe whether an activity is aimed at increasing a level of understanding or whether it involves manipulating information to achieve a tangible task. The dichotomy of interest in this paper is that of synchronous and asynchronous working, where engineers may work alternately as part of a group or as individuals and where different forms of record are necessary to adequately capture the processes and rationale employed in each mode. This paper introduces complimentary approaches to achieving richer representations of design activities performed synchronously and asynchronously, and through the undertaking of a design based case study, highlights the benefit of each approach. The resulting records serve to provide a more complete depiction of activities undertaken, and provide positive direction for future co-development of the approaches