Optimal gait and form for animal locomotion

By K. Wampler, Zoran Popovic. 2009

176 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Computational Biology & Bioinformatics

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Citations in the corpus

Trust Region Policy Optimization (2015, methodology): “For continuous control problems, methods like CMA have been successful at learning control policies for challenging tasks like locomotion when provided with hand-engineered policy classes with low-dimensional parameterizations (Wampler & Popovi´c, 2009).”
High-Dimensional Continuous Control Using Generalized Advantage Estimation (2015, neutral): “This is in contrast with most prior methods for learning locomotion controllers, which typically rely on hand-architected policy classes that explicitly encode notions of balance and stepping [15, 4, 18].”
Robust physics-based locomotion using low-dimensional planning (2010, methodology): “Since trajectory optimization [Witkin and Kass 1988] for full-body locomotion remains very computationally expensive [Liu et al. 2005; Wampler and Popović 2009], we optimize the trajectory of a low-dimensional preview model instead.”
Infinite-Horizon Model Predictive Control for Periodic Tasks with Contacts (2011, methodology): “Local methods of optimization that use a simultaneous representation include multiple shooting [5] and space-time constraints [26], and this approach has been applied to gait design [25, 15].”
Mode-adaptive neural networks for quadruped motion control (2018, background): “…approaches where the motion is optimized based on physical properties such as torques, momentum and feasibility [Levine and Popović 2012; Wampler and Popović 2009; Wampler et al. 2014], and torque-based approaches where the body is directly driven by torques [Coros et al. 2011; Liu and…”
Learning symmetric and low-energy locomotion (2018, methodology): “Apart from designing finite state machines or using motion data, reward engineering combined with trajectory optimization has also been frequently applied to generate physically-based character motions [Al Borno et al. 2013; Ha and Liu 2014; Mordatch et al. 2012, 2013; Wampler and Popović 2009].”
Transforming and Projecting Images into Class-conditional Generative Networks (2020, methodology): “s method is used in iGAN [65]. CMA [26]: z is optimized using CMA. ADAM + CMA [26]: z is drawn from the optimized distribution of CMA. The seeds are further optimized with ADAM. ADAM + BasinCMA [58]: z is optimized by alternating CMA and ADAM updates (Section3.4). 13 Method Average of 18 seeds Be”
A direct method for trajectory optimization of rigid bodies through contact (2014, methodology): “ble modes, outer optimization loops have been used to determine the hybrid mode schedule, as in [24].”
Terrain-adaptive bipedal locomotion control (2010, methodology): “CMA has previously been used to successfully solve locomotion and controller problems [Wampler and Popović 2009; Wang et al. 2009].”
Discovery of complex behaviors through contact-invariant optimization (2012, background): “By optimizing contact times for cyclic patterns, [Wampler and Popovic 2009] is able to synthesize gaits for a wide variety of non-humanoid characters from scratch.”