Current State of Legged Robots and Relation to Current Research

This talk was given to a multi-disciplinary project comprised of researchers at Penn, JHU, Berkeley

Transcript

1. Current State of Legged Robots and Relation to Current Research Avik De Cofounder & CTO, Ghost Robotics (Philadelphia) Postdoc (Harvard SEAS)
2. Axes of confusion Modularity Modular vs. monolithic
3. Optimal control (and learning) vs. reactive control: Foresight Optimal control • (Discrete) construction of “value function” • Given • Infinite horizon cost • find instantaneous “good” direction • HJB equation Reactive control • Find a potential/energy-like function 𝑉(𝑥) • “Myopic” energy-like • “All-knowing” NF • … • ~simple to construct (which lends to easy analysis) • No built-in mechanism to account for the “path cost” [cf. VV/MIT project] [Zhong, …, Todorov (2013)]
4. Optimal control in practice (SOA) • Give up hope on globally estimating VF—intractable • Local estimates around “frequented” regions of state space • “Trajectory optimization” -> local estimate around a specific state/input trajectory • Analogies for RL • Bad if perturbed to a different region of state space • Online “re-estimation” of finite-horizon value function -> MPC • Ensures the trajectory is “near” the current state
5. Analytical controller vs. Optimization: Implementation Optimization • Write as min 𝑢 … 𝑠. 𝑡. 𝑢 ∈ 𝑈 • Global solution if feasible space is convex • In practice: • QP of problem size 100—1000 < 1ms on desktop processor • Problem size 10—100 < 1ms on microcontroller Analytical • Inverse dynamics • Use properties of mechanical system -> natural control • Challenges: • Underactuation • Constraints: 𝑢 ∈ 𝑈? Friction? Recall (assuming known VF) • Optimal control wants • Reactive control wants 𝜕𝑉 𝜕𝑥 𝑓 𝑥, 𝑢 < 0
6. Modularity and Hierarchy in Optimal Control Hover controller 𝑢𝑡 = ℎ(𝑥𝑡 ) 𝑢𝑎 = 𝜋+ ∘ ℎ ∘ 𝜋(𝑥𝑎 ) 𝑥𝑡 = 𝜋(𝑥𝑎 ) Hover value function 𝑉𝑡 𝑥𝑡 𝑉 𝑎 = 𝑉𝑡 ∘ 𝜋 𝑥𝑎 + ganch (xa ) WIP
7. Fielded legged robotics: status update Vision-60 (35kg, 12dof) Research Scientific Commercial Jerboa (3kg, 4dof) Minitaur (6kg, 8dof direct drive)
8. Vision 60 design history 2018 2019 v1.0 v2.0 v3.0 Q1 Q1 v3.5 Prototype Product Customer Q2 Q2 Q3 Q3 Q4 Q4
9. Design process => design science • Power consumption • Gear ratio selection • Impact mitigation • Reflected inertia • Bus voltage utilization • Current @ motor controller • … Power consumption (W) Swing Stance Gait kinematics “Simple models” Past data Gait dynamics Forward dynamics Robot kinematic params Robot dynamic params Motor params
10. Efficiency: a fundamental constraint *MIT cheetah is larger, “actuators only” mechanical (not total) COT, carrying a small battery [Seok et. al. (2015)] Robot Cost of Transport Vision 60 v3.5 0.80 Vision 60 v4.0 0.54 Spot Mini 0.91 MIT Cheetah 0.46* Type Power (W) Motors Mechanical 210 Low level electronics Electrical 20 Blind locomotion Algorithmic <1 Gait planner Algorithmic <5 Autonomy Algorithmic 15 Vision 60 v3.5 power budget
11. Modularity through hierarchy ? ? Abstraction benefits • Reduced (re)development • Computational simplification • Model-robustness
12. Dimension reduction and “reflexes” for perturbation rejection
13. Details of these reflexes make a huge difference in practice: outdoors “details” • Slope estimation • Slip detection and handling • Stubbing detection and handling • Early/late contact handling • “Re-swing” reflexes • …
14. Details of these reflexes make a huge difference in practice: indoors
15. Modularity through hierarchy ? ? Posture control Reflexes
16. Control as reduction (anchoring) + composition [Full and Koditschek (1999)] “Templates and anchors…” [De and Koditschek (IJRR 2018)]
17. Reduction (anchoring) exposes templates: reactive quadrupedal walking Event-driven coupled swing leg oscillators WIP Trot walk (crawl-ish) walk
18. Can go further: templates are inevitable • Subject to anchoring posture control, • With sufficient actuated DOFs, • (degrades gracefully with fewer) • Reduced dynamics at least contain IP. [De, Topping, Kod (in prep)]
19. Embedding pitch-steady target dynamics on floating torso models • “Zero manifold” = pitch-steady locomotion (e.g. walking) • Render ZM attracting and invariant Limitations: • Restriction dynamics are affected by anchoring force • Form of restriction dynamics depends on virtual constraint choice Valid zero dynamics [De, Topping, Kod (in prep)] [Westervelt et. al (2007)]
20. Input-decoupled anchoring • Can find reduced coordinates 𝑟 𝑞 s.t. 𝑢𝑎𝑛𝑐ℎ does not appear in ሷ 𝑟 • 𝑟 𝑞 is ~ virtual leg pos • SLIP dynamics are exactly embedded! A new kind of anchoring Input-decoupled anchoring with actuated IP template behavior Floating torso model 𝑥 ∈ 𝑆𝐸(2) Conventional anchoring Input- decoupled anchoring Invariant+attracting pitch-stable manifold (conventional anchoring/ZD) [Full & Kod (1999)] [Westervelt et al (2007)] [De, Topping, Kod (in prep)]
21. Application to open-loop control of leaping [De, Topping, Kod (in prep)] • Use IP template behavior to design open- loop leaping controllers • Together with provably correct anchoring • Application to leaping for mobile manipulation (preliminary)
22. Modularity through hierarchy ? Template control Template dynamics Posture control Reflexes “Blind” template behavior Sensor head ?
23. Combining reflexes with anticipatory planning (WIP) • Receding horizon planning • With template model • Hierarchical dimension reduction enables real- time solutions, robustness to model uncertainty
24. Conclusion • Axes of confusion • Modularity is a time-investment that saves time, computational effort, improves robustness • Ghost is hiring -> careers@ghostrobotics.io​ What do the animals do?