Behavior-based Approach to Humanoid Robot Manipulation

1. A Behavior Based Approach to Humanoid Robot ManipulationAaron Edsinger-GonzalesMIT Computer Science and Artificial Intelligence LaboratoryHumanoid Robotics Groupedsinger@csail.mit.edu

2. Roadmap • Overview and motivation of approach • The robot platform • Compliant and force sensing manipulation • Behavior based decomposition • Experimental results

3. Roadmap • Overview and motivation of approach • The robot platform • Compliant and force sensing manipulation • Behavior based decomposition • Experimental results

4. Research Direction • Behavior based manipulation • Compliant and force controlled manipulators • Learning predictive sensorimotor models during • manipulation engagements

5. Research Direction • Behavior based manipulation • Compliant and force controlled manipulators • Learning predictive sensorimotor models during • manipulation engagements

6. Research Direction • Behavior based manipulation • Force based behaviors • Compliant and force controlled manipulators • Interaction in unknown environments • Intuitive control strategies

7. Manipulation without Models • Model based manipulation: look-think-act decomposition • Unstructured environments: break model based manipulation systems • Our approach: • Tightly coupled force-based interaction • Interaction is not modeled • Overall behavior is an emergent consequence • of the interaction

8. Manipulation without Models • Model based manipulation: look-think-act decomposition • Unstructured environments: break model based manipulation systems • Our approach: • Tightly coupled force-based interaction • Interaction is not modeled • Overall behavior is an emergent consequence • of the interaction Behavior based architectures can support this approach

9. Navigation vs Manipulation • Packbot and Mars Soujourner • Applied success of behavior based control architectures • Passively engaged with the world. • Embodied intelligence requires active interaction • Manipulation driven perception • Affordance grounding

10. Navigation Manipulation

11. A Path Forward Precondition the manipulation engagement to increase the likelihood of success. • Anticipatory sensorimotor behaviors • Preshaping of the grasp • Prestiffening of the arm • Introduce compliant elements in the manipulator • Stabilizing effect of high-bandwidth low impedance • Force based control of the manipulator • Remove absolute knowledge of the object pose

12. Roadmap • Overview and motivation of approach • The robot platform • Compliant and force sensing manipulation • Behavior based decomposition • Experimental results

13. Domo Designed to support our approach to robot manipulation • 2 6 DOF force sensing Arms • 2 4 DOF force sensing Hands • 9 DOF Active Vision Head • 29 DOF Total • 51 Proprioceptive/Force Sensors • 24 Tactile Sensors • 2 Firewire CCD Cameras • 20 Kg

14. Mechatronics • Embedded control electronics • 5 DSP CANBus Network • High bandwidth control • Electromechanical robustness • Cable-drive • Interior cable routing • Compliant interaction • Good mass distribution • Linux cluster • Visual perception • Cognitive architecture

15. System Architecture

16. Active Vision Head • 9 DOF • 2 DOF SEA U-Joint Neck • 1 DOF Pan • 2 DOF Differential Upper Roll/Tilt • 1 DOF Eye Tilt • 2 DOF Eye Pan • 1 DOF Eyelids • 2 CCD Firewire cameras

17. Hands • 4 Force Sensing Compliant Actuators • 4 force controlled DOF • 3 mechanically coupled DOF • 3 passively compliant DOF • 12 FSR tactile sensors • 1 DOF finger spread • Force controlled • Angular and force sensors for each active DOF • Embedded interface electronics • Modular Design

18. Arms 6 DOF using Series Elastic Actuators Force sensing and joint angle sensing Cable drive construction moves mass to shoulder ~15W power consumption during ballistic movements 2 Kg

19. Roadmap • Overview and motivation of approach • The robot platform • Compliant and force sensing manipulation • Behavior based decomposition • Experimental results

20. Compliant and Force Sensitive Manipulators • Compliance: An elastic element between the motor output and the joint allows for local adaptation and absorption of forces. • Force Sensitive: • Directly sense the torque at each joint • Simulation of the viscoelastic properties of natural muscle • Intuitive control architectures

21. Compliant and Force Sensitive Manipulators • Compliance: An elastic element between the motor output and the joint allows for local adaptation and absorption of forces. • Force Sensitive: • Directly sense the torque at each joint • Simulation of the viscoelastic properties of natural muscle • Intuitive control architectures Humans are good force-controllers, poor position controllers. Manipulation without explicit models. Safe robot-world interface.

22. Series Elastic and Force Sensing Compliant Actuators Force Sensing Compliant Actuator Series Elastic Actuator

23. Series Elastic and Force Sensing Compliant Actuators F=-kx

24. Series Elastic and Force Sensing Compliant Actuators • Mechanically simple • Improved stability • Shock tolerance • Highly backdrivable • Low-grade components • Low impedance at high frequencies

25. Virtual Model Control • Pratt et al. [Virtual Model Control: An Intuitive Approach for Bipedal Locomotion] • Intuitive physical metaphors: springs and dampers • Virtual springs: between the manipulator and environment. • Parallel control: add to the natural dynamics of the arm • Layering of springs: supports behavior based approach

26. Roadmap • Overview and motivation of approach • The robot platform • Compliant and force sensing manipulation • Behavior based decomposition • Experimental results

27. Behavior Based Approaches to Manipulation • Cog [MIT, Marjanovic, “Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things “, 2003] • Dexter[U.Mass Amherst, Grupen et al., “Developing Haptic and Visual Categories for Reaching and Grasping with a Humanoid Robot”, 2000] • Babybot[U. Genoa, Metta “Babybot: a Study into Sensorimotor Development”,2000]

28. Manipulation Decomposition • Components are not cleanly separated in practice • All are tightly coupled through perceptual processes to the world. • Minimal “look-think-act” approach Deciding on Actions Positioning Sensors Perception Placing body Grasping Force operations Transfer Disengaging Detecting Failures

29. Manipulation Decomposition • Components are not cleanly separated in practice • All are tightly coupled through perceptual processes to the world. • Minimal “look-think-act” approach Deciding on Actions Positioning Sensors Perception Placing body Grasping Force operations: Transfer Disengaging Detecting Failures Explored in this initial work.

30. Manipulation Decomposition Deciding on Actions: A selection mechanism or may be prespecified Positioning Sensors: Gain appropriate views of the task by dynamic Perception: Understanding of where objects are and their properties Placing body: Pose adaptation enhances manipulator workspace Grasping: Stable grip through tactile and visual integration Force operations: Modulation of interaction forces to object dynamics Transfer: Transport to desired location while avoiding collisions Disengaging: Release of the object at the correct location and pose. Detecting Failures: Perceptually guided detection of failed actions which feeds back into the action selection process.

31. Roadmap • Overview and motivation of approach • The robot platform • Compliant and force sensing manipulation • Behavior based decomposition • Experimental results

32. Visually Guided Reaching and Grasping • Single fixed camera • One arm and one hand

33. Visually Guided Reaching and Grasping

34. Target Detection

35. Hand Localization

36. Force Behaviors

37. Grasping Behaviors

38. Results

39. Future Work • Computation of target depth information • More general target segmentation • Richer set of grasping behaviors incorporating tactile information • Reaching in 3D • Development of anticipatory sensorimotor behaviors