Presentation on theme: "A M M W O R K S H O P John Hollerbach Oussama Khatib Vijay Kumar Al Rizzi Daniela Rus Control and Representation Vijay Kumar University of Pennsylvania."— Presentation transcript:
A M M W O R K S H O P John Hollerbach Oussama Khatib Vijay Kumar Al Rizzi Daniela Rus Control and Representation Vijay Kumar University of Pennsylvania NSF/NASA AMM Workshop March 10-11, 2005 Houston.
NSF/NASA AMM Workshop Outline State-of-art Historical perspective (nostalgic memories) Accomplishments in robot control Summary of last 21 years (WTEC study) Recent, specific contributions (somewhat biased) Challenges Panelists Discussion What are the intellectual problem areas we should address? Infrastructure? Can we can rally around these?
NSF/NASA AMM Workshop Historical Perspective 40+ years of industrial robotics >20 years of robotics as an academic discipline ~13 years of mobile manipulation 40 years of industrial robotics General Motors 1961 Unimate Rus SarcosARC Hollerbach Mobility & Manipulation
NSF/NASA AMM Workshop The Real Agenda for AMM Mobility Unstructured environments Manipulation Physical interaction with the environment Closely coupled perception/action Not physically grounded Dynamics is important Autonomy Teleoperation (and therefore haptics) Supervised Autonomy Autonomy Haptics John Hollerbach Humanoids Oussama Khatib Perception/Action Al Rizzi Distributed/Modular Daniela Rus
NSF/NASA AMM Workshop Robotics in the news this week WSJ, 3/7 “…teleoperation with time delays is a vexing problem in robotics…” “…because of the lag, it’s inevitable that the human operator will make tiny errors - errors that will in turn cascade into much bigger ones…”
NSF/NASA AMM Workshop Literature Domain ~8-10% manipulation ~3-4% grasping ~30-35% mobility Remaining are on medical, manufacturing, industrial, sensor or “methodology” Disclaimer: This is not a scientific study! Conferences surveyed: ICRA 1984-86, 1998-2004 Control/representation Model based (~15%) Data driven approaches (~5%) Counted papers relevant to manipulation and mobility
NSF/NASA AMM Workshop Literature (Compared to 1984) Domain ~10% manipulation ~4% grasping ~35% mobility Remaining are on medical, manufacturing, industrial, sensor or “methodology” Disclaimer: This is not a scientific study! Conferences surveyed: ICRA 1984-86, 1998-2004 Control/representation Model based (~15%) Data driven approaches (~5%) Counted papers relevant to manipulation and mobility (40%) (4%) (40%) (3 %) Total number of papers = 74 ~9880 ICRA papers to date
NSF/NASA AMM Workshop Major Advances Academic/Government Labs Inverse dynamics: application of feedback linearization to serial robots, now routinely used in industrial manipulators (e.g., ABB) Time optimal control: along a path subject to dynamics, velocity and acceleration constraints, also used in industrial manipulators Adaptive robot control: model based adaptive control with global stability guarantee Nonholonomic control: control using time varying feedback or cyclic input, application of differential flat system theory, mostly applied to mobile robots and under-actuated robots. [Wen and Maciejewski, 04] !!! !? !!! Disclaimer: Not a survey of accomplishments/needs for AMM
NSF/NASA AMM Workshop Major Advances (Cont.) Flexible joint robot modeling and control: Application of feedback linearization to flexible joint robots, applied to some industrial arms. Teleoperation: wave variable based control for delay robustness. Guarantee stability, but user would feel delayed response. Order N simulation: Application of order N computation to forward and inverse dynamics. Essential for large number degrees of freedom, e.g., robot with flexible link, micro-robots. Hybrid force/position, impedance control: Simultaneous regulation of motion and force, applied to machining, assembly, haptic feedback, multi-finger control ?! ! !!!
NSF/NASA AMM Workshop AMM Survey (?) ICRA 2000: Grasping and Manipulation Review [Bicchi and Kumar, 2000] Saturation of the area? All problems solved Not interesting Not relevant
NSF/NASA AMM Workshop Two other possibilities Problems are too hard Or Nobody is interested in funding this work!
NSF/NASA AMM Workshop Significant Accomplishments: Industry Fanuc 20% market share 1800 employees (1300 in research labs, 10 Ph.Ds) 10,000 robots Technology provides the competitive edge Before z servo motors/amplifiers Now z collision detection, compliance control, payload inertia/weight identification, force/vision sensing/integration robots assemble/test robots beyond human performance And mobile manipulation! Technology transfer does happen! Remember those ~9880 ICRA papers?
NSF/NASA AMM Workshop Results we can build on… (a parochial view) Modeling/controlling humanoids Dynamic manipulation and locomotion Cooperative mobile manipulation Distributed locomotion (and manipulation) systems Haptics and teleoperation
NSF/NASA AMM Workshop Humanoid dynamics and control Biomechanics for robotics Realistic models Minimum principles leading to realistic motions [Khatib] Integration (composition) Integrated control of reach and posture Task space versus posture space
NSF/NASA AMM Workshop Humanoid dynamics and control Whole-body multi-contact control Multiple frictional contacts Models z Posture z Legs z Locomotion [Khatib]
NSF/NASA AMM Workshop Locomotion and Dexterous Manipulation Dynamic manipulation and locomotion Intermittent interaction Passive dynamics Reactive control [Rizzi]
NSF/NASA AMM Workshop Significant Accomplishments: Academia Multiple Mobile Manipulators Multiple frictional contacts Maintaining closure [Khatib] [Kumar][Rus]
NSF/NASA AMM Workshop M 3 Modular Mobile Manipulation Self-organizing, self-assembling, self-repair Adapt structure Multiple Functionalities Can do work [Rus]
NSF/NASA AMM Workshop And yet significant challenges remain! No successful field deployment of mobile manipulators Example: Robotic servicing of Hubble (NAS Committee: Brooks, Rock, Kumar) ETS-VII (JAXA/NASA) z Model-based tele-manipulation z Visual servoing for acquisition of non cooperative targets No robot (product) capable of physical interactions in unstructured environment Example: Assistive Robotics
NSF/NASA AMM Workshop Assistive Robotics Impact > 5 million wheelchair users* in the U.S. > 730,000 strokes/year (2/3 disabled five years after stroke), > $50B/year > 10,000 SCI/year (most < 20 yrs old) Realistic Human-in-the-loop No competing technology z Many other overarching challenges *Inter Agency Working Group on Assistive Technology Mobility Devices
NSF/NASA AMM Workshop Current technology Artificial limbs: peg legs, hook hand Crutches, canes, walkers Wheelchairs Environmental control systems Remote control Many, many customized products
NSF/NASA AMM Workshop Significant Challenges, Problems 1. New hardware, systems 2. Modeling/control 3. Composition, synthesis 4. Model-based versus data-based
NSF/NASA AMM Workshop pHRI: Safety and Performance >20 cm compliant covering Challenge: 10x reduction in effective inertia [Khatib]
NSF/NASA AMM Workshop Haptic Interfaces and Mobility Energetic/force interactions between robots and humans Control simulations or real devices Personal assist or amplification devices Rehabilitation or exercise robots Need haptic interfaces that allow manipulation while walking Psychological argument for VR Need to control robots that can reach/grasp/manipulate/lean/kick/push [Hollerbach]
NSF/NASA AMM Workshop Portable Haptic Interfaces Body-worn systems Powered exoskeleton Ground-based system with locomotion interface
NSF/NASA AMM Workshop Representation and Control Physics of environmental interaction Distributed interaction z Whole arm/leg/body Task representation for non-rigid interaction and manipulation Control and task allocation of multi-function appendages (feet, legs, hands, arms, etc.) Composition of closed-loop (perception/action) behaviors [Rizzi]
NSF/NASA AMM Workshop Composition of Behaviors: Example Four behaviors (closed-loop controllers) Pre-shape (open/close) Grasp/release Reach/retract Go to (move)
NSF/NASA AMM Workshop Distributed Approaches and Modularity Distributed Control Heterogeneous systems with active modules, passive modules, and tools for mobile manipulation Mobile sub-assemblies and hierarchical control Thanks to Hod Lipson
NSF/NASA AMM Workshop Future Concept for Modular Robots in Mobile Manipulation Concept: self-assembly with active grippers and rods Concept: mobile sub-assemblies note: mobile manipulation with dynamic kinematic topology for c-space Concept: self-inspection and self-repair with tools
NSF/NASA AMM Workshop Distributed Approaches and Modularity Challenges Control for systems with dynamic kinematic topology Under-constraint systems with continuum of solutions Control for systems with changing c-space Geometrically-driven posture control Control for keeping balance and structural integrity Optimal morphologies for tasks Uncertainty and Error in Modular Systems Cooperative approach to error recovery in module and structure alignment, connections, assembly, and repair Dynamical models with uncertainty
NSF/NASA AMM Workshop Model-based vs. Data Driven Control/representation Model based (~15%) Data driven approaches (~5%) Dynamic models are getting more complicated and increasingly sensitive to parameters (uncertainty) Emphasize completely data-driven approaches
NSF/NASA AMM Workshop Discussion Are there a set of basic research questions that We can rally around? Are unique to autonomous mobile manipulation? Are critical? High-impact? If so, can we create a new research program? How do we sell it? How do we take this to the next step? Balance basic research high-caliber applied research How do we make robotics a “big science”?
NSF/NASA AMM Workshop Intellectual Basis for New Program in Autonomous Mobile Manipulation Closed-loop behaviors Perception-action loops Vision-based control Composition of behaviors Sequential Parallel, hierarchical Task description language Formal semantics Uncertainty Understanding and characterizing uncertainty Data-driven approaches Teleoperation and haptics Integration mobility with manipulation Can it be a Tether-esque program?