MPC with Sensor-Based Online Cost Adaptation

Model predictive control is a powerful tool to generate complex motions for robots. However, it often requires solving non-convex problems online to produce rich behaviors, which is computationally expensive and not always practical in real time. Additionally, direct integration of high dimensional sensor data (e.g. RGB-D images) in the feedback loop is challenging with current state-space methods. This paper aims to address both issues. It introduces a model predictive control scheme, where a neural network constantly updates the cost function of a quadratic program based on sensory inputs, aiming to minimize a general non-convex task loss without solving a non-convex problem online. By updating the cost, the robot is able to adapt to changes in the environment directly from sensor measurement without requiring a new cost design. Furthermore, since the quadratic program can be solved efficiently with hard constraints, a safe deployment on the robot is ensured. Experiments with a wide variety of reaching tasks on an industrial robot manipulator demonstrate that our method can efficiently solve complex non-convex problems with high-dimensional visual sensory inputs, while still being robust to external disturbances.Comment: 6 Pages, 5 Figure

Minimum Jerk Trajectory Planning for Trajectory Constrained Redundant Robots

In this dissertation, we develop an efficient method of generating minimal jerk trajectories for redundant robots in trajectory following problems. We show that high jerk is a local phenomenon, and therefore focus on optimizing regions of high jerk that occur when using traditional trajectory generation methods. The optimal trajectory is shown to be located on the foliation of self-motion manifolds, and this property is exploited to express the problem as a minimal dimension Bolza optimal control problem. A numerical algorithm based on ideas from pseudo-spectral optimization methods is proposed and applied to two example planar robot structures with two redundant degrees of freedom. When compared with existing trajectory generation methods, the proposed algorithm reduces the integral jerk of the examples by 75% and 13%. Peak jerk is reduced by 98% and 33%. Finally a real time controller is proposed to accurately track the planned trajectory given real-time measurements of the tool-tip\u27s following error

Human-Robot Collaboration in Automotive Assembly

In the past decades, automation in the automobile production line has significantly increased the efficiency and quality of automotive manufacturing. However, in the automotive assembly stage, most tasks are still accomplished manually by human workers because of the complexity and flexibility of the tasks and the high dynamic unconstructed workspace. This dissertation is proposed to improve the level of automation in automotive assembly by human-robot collaboration (HRC). The challenges that eluded the automation in automotive assembly including lack of suitable collaborative robotic systems for the HRC, especially the compact-size high-payload mobile manipulators; teaching and learning frameworks to enable robots to learn the assembly tasks, and how to assist humans to accomplish assembly tasks from human demonstration; task-driving high-level robot motion planning framework to make the trained robot intelligently and adaptively assist human in automotive assembly tasks. The technical research toward this goal has resulted in several peer-reviewed publications. Achievements include: 1) A novel collaborative lift-assist robot for automotive assembly; 2) Approaches of vision-based robot learning of placing tasks from human demonstrations in assembly; 3) Robot learning of assembly tasks and assistance from human demonstrations using Convolutional Neural Network (CNN); 4) Robot learning of assembly tasks and assistance from human demonstrations using Task Constraint-Guided Inverse Reinforcement Learning (TC-IRL); 5) Robot learning of assembly tasks from non-expert demonstrations via Functional Objective-Oriented Network (FOON); 6) Multi-model sampling-based motion planning for trajectory optimization with execution consistency in manufacturing contexts. The research demonstrates the feasibility of a parallel mobile manipulator, which introduces novel conceptions to industrial mobile manipulators for smart manufacturing. By exploring the Robot Learning from Demonstration (RLfD) with both AI-based and model-based approaches, the research also improves robots’ learning capabilities on collaborative assembly tasks for both expert and non-expert users. The research on robot motion planning and control in the dissertation facilitates the safety and human trust in industrial robots in HRC

A Conflict-driven Interface between Symbolic Planning and Nonlinear Constraint Solving

Robotic planning in real-world scenarios typically requires joint optimization of logic and continuous variables. A core challenge to combine the strengths of logic planners and continuous solvers is the design of an efficient interface that informs the logical search about continuous infeasibilities. In this paper we present a novel iterative algorithm that connects logic planning with nonlinear optimization through a bidirectional interface, achieved by the detection of minimal subsets of nonlinear constraints that are infeasible. The algorithm continuously builds a database of graphs that represent (in)feasible subsets of continuous variables and constraints, and encodes this knowledge in the logical description. As a foundation for this algorithm, we introduce Planning with Nonlinear Transition Constraints (PNTC), a novel planning formulation that clarifies the exact assumptions our algorithm requires and can be applied to model Task and Motion Planning (TAMP) efficiently. Our experimental results show that our framework significantly outperforms alternative optimization-based approaches for TAMP

Policy search for imitation learning

Efficient motion planning and possibilities for non-experts to teach new motion primitives are key components for a new generation of robotic systems. In order to be applicable beyond the well-defined context of laboratories and the fixed settings of industrial factories, those machines have to be easily programmable, adapt to dynamic environments and learn and acquire new skills autonomously. Reinforcement learning in principle solves those learning issues but suffers from the curse of dimensionality. When dealing with complex environments and highly agile hardware platforms like humanoid robots in large or possibly continuous state and action spaces, the reinforcement framework becomes computationally infeasible. In recent publications, parametrized policies have been employed to face this problem. One of them, Policy Improvement with Path Integrals (PI^2), has been derived from the transformation of the Hamilton-Jacobi-Bellman (HJB) equation of stochastic optimal control into a path integral using the Feynmann Kac theorem. Applications of PI^2 are so far limited to Dynamic Movement Primitives (DMP) to parametrize the motion policy. Another policy parametrization, the formulation of motion primitives as solution of an optimization-based planner has been widely used in other fields (e.g. inverse optimal control) and offers compelling possibilities to formulate characteristic parts of a motion in an abstract sense without specifying too much problem-specific geometry. Imitation learning or learning from demonstration can be seen as a way to bootstrap the acquisition of new behavior and as an efficient way to guide the policy search into a desired direction. Nevertheless, due to imperfect demonstrations, which might be incomplete or contradictory and also due to noise, the learned behavior might be insufficient. As observed in the animal kingdom, a final trial-and-error phase guided by the cost and reward of a specific behavior is necessary to obtain a successful behavior. Interestingly, the reinforcement learning framework might offer the tools to govern both learning methods at the same time. Imitation learning can be reformulated as reinforcement learning under a specific reward function, allowing the combination of both learning methods. In this work, the concept of probability-weighted averaging of policy roll-outs as seen in PI^2 is combined with an optimization-based policy representation. The reinforcement learning toolbox and direct policy search is utilized in a way that allows both imitation learning based on arbitrary demonstration types and the imposition of additional objectives on the learned behavior. A black box evolutionary algorithm, Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), which can be shown to be closely related to the approach in PI2 is leveraged to explore the parameter space. This work will experimentally evaluate the suitability of this algorithm for learning motion behavior on a humanoid upper body robotic system. We will focus on learning from different types of demonstrations. The formulation of the reward function for reinforcement learning will be depicted and multiple test scenarios in 2D and 3D will be presented. Finally, the capability of this approach to learn and improve motion primitives is demonstrated on a real robotic system within an obstacle test scenario