Topology-Guided Path Integral Approach for Stochastic Optimal Control in Cluttered Environment

This paper addresses planning and control of robot motion under uncertainty that is formulated as a continuous-time, continuous-space stochastic optimal control problem, by developing a topology-guided path integral control method. The path integral control framework, which forms the backbone of the proposed method, re-writes the Hamilton-Jacobi-Bellman equation as a statistical inference problem; the resulting inference problem is solved by a sampling procedure that computes the distribution of controlled trajectories around the trajectory by the passive dynamics. For motion control of robots in a highly cluttered environment, however, this sampling can easily be trapped in a local minimum unless the sample size is very large, since the global optimality of local minima depends on the degree of uncertainty. Thus, a homology-embedded sampling-based planner that identifies many (potentially) local-minimum trajectories in different homology classes is developed to aid the sampling process. In combination with a receding-horizon fashion of the optimal control the proposed method produces a dynamically feasible and collision-free motion plans without being trapped in a local minimum. Numerical examples on a synthetic toy problem and on quadrotor control in a complex obstacle field demonstrate the validity of the proposed method.Comment: arXiv admin note: text overlap with arXiv:1510.0534

Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image

In this paper, we propose a deep convolutional recurrent neural network that predicts action sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g. first-order logic) with continuous motion planning such as nonlinear trajectory optimization. Due to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we develop a neural network which, based on an initial image of the scene, directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. A key aspect is that our method generalizes to scenes with many and varying number of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene in images as input to the neural network, instead of a fixed feature vector. Results show runtime improvements of several magnitudes. Video: https://youtu.be/i8yyEbbvoEkComment: Robotics: Science and Systems (R:SS) 202

Learning to solve sequential physical reasoning problems from a scene image

In this article, we propose deep visual reasoning, which is a convolutional recurrent neural network that predicts discrete action sequences from an initial scene image for sequential manipulation problems that arise, for example, in task and motion planning (TAMP). Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g., first-order logic) with continuous motion planning such as nonlinear trajectory optimization. The action sequences represent the discrete decisions on a symbolic level, which, in turn, parameterize a nonlinear trajectory optimization problem. Owing to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we introduce deep visual reasoning: based on a segmented initial image of the scene, a neural network directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. Our method generalizes to scenes with many and varying numbers of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene and the goal in (segmented) images as input to the neural network, instead of a fixed feature vector. We show that the framework can not only handle kinematic problems such as pick-and-place (as typical in TAMP), but also tool-use scenarios for planar pushing under quasi-static dynamic models. Here, the image-based representation enables generalization to other shapes than during training. Results show runtime improvements of several orders of magnitudes by, in many cases, removing the need to search over the discrete action sequences.DFG, 390523135, EXC 2002: Science of Intelligence (SCIoI

Incidence and Risk Factors Associated with Superior Mesenteric Artery Syndrome following Surgical Correction of Scoliosis

STUDY DESIGN: Retrospective study. PURPOSE: To more accurately determine the incidence and clarify risk factors. OVERVIEW OF LITERATURE: Superior mesenteric artery syndrome is one of the possible complications following correctional operation for scoliosis. However, when preliminary symptoms are vague, the diagnosis of superior mesenteric artery syndrome may be easily missed. METHODS: We conducted a retrospective study using clinical data from 118 patients (43 men and 75 women) who underwent correctional operations for scoliosis between September 2001 and August 2007. The mean patient age was 15.9 years (range 9~24 years). The risk factors under scrutiny were the patient body mass index (BMI), change in Cobb's angle, and trunk length. RESULTS: The incidence of subjects confirmed to have obstruction was 2.5%. However, the rate increased to 7.6% with the inclusion of the 6 subjects who only showed clinical symptoms of obstruction without confirmative study. The BMI for the asymptomatic and symptomatic groups were 18.4+/-3.4 and 14.6+/-3, respectively. The change in Cobb's angle for the asymptomatic and symptomatic groups were 24.8+/-13.6 degrees and 23.4+/-9.1 degrees , respectively. The change in trunk length for the asymptomatic and symptomatic groups were 2.3+/-2.1 cm and 4.5+/-4.8 cm, respectively. Differences in Cobb's angle and the change in trunk length between the two groups did not reach statistical significance, although there was a greater increase in trunk length for the symptomatic group than for the asymptomatic group. CONCLUSIONS: Our study shows that the incidence of superior mesenteric artery syndrome may be greater than the previously accepted rate of 4.7%. Therefore, in the face of any early signs or symptoms of superior mesenteric artery syndrome, prompt recognition and treatment are necessaryope