人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

Observing Interaction: An Introduction to Sequential Analysis, Roger Bakeman, John M. Gottman. Cambridge University Press, Cambridge (1986), ixv, +221. Price £25.00 hardback, £7.95 paperback

Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive. In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes. The approach is applied to the ANYmal robot, a sophisticated medium-dog-sized quadrupedal system. Using policies trained in simulation, the quadrupedal machine achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than before, and recovering from falling even in complex configurations.

/pdf/learning-agile-and-dynamic-motor-skills-for-legged-robots-41hdxn2m1m.pdf

Learning agile and dynamic motor skills for legged robots

This paper presents an implementation of model predictive control (MPC) to determine ground reaction forces for a torque-controlled quadruped robot. The robot dynamics are simplified to formulate the problem as convex optimization while still capturing the full 3D nature of the system. With the simplified model, ground reaction force planning problems are formulated for prediction horizons of up to 0.5 seconds, and are solved to optimality in under 1 ms at a rate of 20–30 Hz. Despite using a simplified model, the robot is capable of robust locomotion at a variety of speeds. Experimental results demonstrate control of gaits including stand, trot, flying-trot, pronk, bound, pace, a 3-legged gait, and a full 3D gallop. The robot achieved forward speeds of up to 3 m/s, lateral speeds up to 1 m/s, and angular speeds up to 180 deg/sec. Our approach is general enough to perform all these behaviors with the same set of gains and weights.

Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control

We present a single trajectory optimization formulation for legged locomotion that automatically determines the gait sequence, step timings, footholds, swing-leg motions, and six-dimensional body motion over nonflat terrain, without any additional modules. Our phase-based parameterization of feet motion and forces allows to optimize over the discrete gait sequence using only continuous decision variables. The system is represented using a simplified centroidal dynamics model that is influenced by the feet's location and forces. We explicitly enforce friction cone constraints, depending on the shape of the terrain. The nonlinear programming problem solver generates highly dynamic motion plans with full flight phases for a variety of legged systems with arbitrary morphologies in an efficient manner. We validate the feasibility of the generated plans in simulation and on the real quadruped robot ANYmal. Additionally, the entire solver software TOWR , which used to generate these motions is made freely available.

/pdf/gait-and-trajectory-optimization-for-legged-systems-through-1hrl1mlqt7.pdf

Gait and Trajectory Optimization for Legged Systems Through Phase-Based End-Effector Parameterization

This paper presents a method to estimate external forces exerted on a manipulator during motion, avoiding the use of a sensor. The method is based on task-oriented dynamics model learning and a robust disturbance state observer. The combination of both leads to an efficient torque observer that can be incorporated to any control scheme. The use of a learning-based approach avoids the need of analytical models of joints' friction or Coriolis dynamics effects.

/pdf/external-force-estimation-during-compliant-robot-2eg1pyqdme.pdf

External force estimation during compliant robot manipulation

This letter combines the fast zero-moment-point approaches that work well in practice with the broader range of capabilities of a trajectory optimization formulation, by optimizing over body motion, footholds, and center of pressure simultaneously. We introduce a vertex-based representation of the support-area constraint, which can treat arbitrarily oriented point-, line-, and area-contacts uniformly. This generalization allows us to create motions, such as quadrupedal walking, trotting, bounding, pacing, combinations, and transitions between these, limping, bipedal walking, and push recovery all with the same approach. This formulation constitutes a minimal representation of the physical laws (unilateral contact forces) and kinematic restrictions (range of motion) in legged locomotion, which allows us to generate diverse motions in less than a second. We demonstrate the feasibility of the generated motions on a real quadruped robot.

Fast Trajectory Optimization for Legged Robots Using Vertex-Based ZMP Constraints

This paper studies existing direct transcription methods for trajectory optimization for robot motion planning. These methods have demonstrated to be favorable for planning dynamically feasible motions for high dimensional robots with complex dynamics. However, an important disadvantage is the augmented size and complexity of the associated multivariate nonlinear programming problem (NLP). Due to this complexity, preliminary results suggest that these methods are not suitable for performing the motion planning for high degree of freedom (DOF) robots online. Furthermore, there is insufficient evidence about the successful use of these approaches on real robots. To gain deeper insight into the performance of trajectory optimization methods, we analyze the influence of the choice of different transcription techniques as well as NLP solvers on the run time. There are different alternatives for the problem transcription, mainly determined by the selection of the integration rule. In this study these alternatives are evaluated with a focus on robotics, measuring the performance of the methods in terms of computational time, quality of the solution, sensitivity to open parameters and complexity of the problem. Additionally, we compare two optimization methodologies, namely Sequential Quadratic Programming (SQP) and Interior Point Methods (IPM), which are used to solve the transcribed problem. As a performance measure as well as a verification of using trajectory optimization on real robots, we are presenting hardware experiments performed on an underactuated, nonminimal-phase, ball-balancing robot with a 10 dimensional state space and 3 dimensional input space. The benchmark tasks solved with the real robot take into account path constraints and actuation limits. These experiments constitute one of very few examples of full-state trajectory optimization applied to real hardware.

Evaluating Direct Transcription and Nonlinear Optimization Methods for Robot Motion Planning

We present an algorithm that generates walking motions for quadruped robots without the use of an explicit footstep planner by simultaneously optimizing over both the Center of Mass (CoM) trajectory and the footholds. Feasibility is achieved by imposing stability constraints on the CoM related to the Zero Moment Point and explicitly enforcing kinematic constraints between the footholds and the CoM position. Given a desired goal state, the problem is solved online by a Nonlinear Programming solver to generate the walking motion. Experimental trials show that the algorithm is able to generate walking gaits for multiple steps in milliseconds that can be executed on a real quadruped robot.

/pdf/online-walking-motion-and-foothold-optimization-for-5gsjpjzft9.pdf

Online walking motion and foothold optimization for quadruped locomotion

Reference EPFL-CONF-228447 URL: http://roboticsconference.org/program/papers/41/ Record created on 2017-05-29, modified on 2017-05-29

http://www.roboticsproceedings.org/rss13/p42.pdf

Diego Pardo

Papers

External force estimation during compliant robot manipulation

Fast Trajectory Optimization for Legged Robots Using Vertex-Based ZMP Constraints

Evaluating Direct Transcription and Nonlinear Optimization Methods for Robot Motion Planning

Online walking motion and foothold optimization for quadruped locomotion

Hybrid direct collocation and control in the constraint-consistent subspace for dynamic legged robot locomotion