Ambarish Goswami: Safe fall strategies for humanoid robots
ambarish.com Ambarish Goswami's Current Research
Home/ Current Research/ Safe fall strategies for humanoid robots
Contact Info

Safe fall strategies for humanoid robots


Safety is a primary concern that must be addressed before humanoid robots can freely exist in human surroundings. Out of a number of possible situations where safety becomes an issue, one that involves a fall is particularly worrisome. Fall from an upright posture can cause damage to the robot, to delicate and expensive objects in the surrounding or to a human being. Regardless of the substantial progress in humanoid robot balance control strategies, the possibility of a fall remains real, even unavoidable. Yet, a comprehensive study of humanoid fall and prescribed fall strategies are rare.

One can ignore the possibility of a fall and wishfully hope that its effects will not be serious. However, failure studies, such as in car crash, have taught us against behaving according to this instinct. In fact, planning and simulation of failure situations can have enormous benefits, including system design improvements, and support for user safety and confidence. Following this philosophy we closely focus our attention to the phenomenon of humanoid fall and attempt to develop a comprehensive control strategy to deal with this undesired and traumatic ``failure'' event.


humanoid robot falls among multiple objects, no control
Humanoid robot falls among multiple objects. With no control, the robot nmay hit an object.

humanoid robot successfully avoids hitting	object with fall controller
Humanoid robot can successfully avoid hitting surrounding objects using a fall controller.


A humanoid fall may be caused due to unexpected or excessive external forces, unusual or unknown slipperiness, slope or profile of the ground, causing the robot to slip, trip or topple. In these cases the disturbances that threaten balance are larger than what the balance controller can handle. Fall can also result from actuator, power or communication failure where the balance controller is partially or fully incapacitated. We currently consider only those situations in which the motor power is retained such that the robot can execute a prescribed control strategy.

A fall controller can target two major objectives independently or in combination: a) fall with a minimum damage and b) change fall direction such that the robot does not hit a certain object or person. Here we introduce a strategy for fall direction change and describe a controller which can achieve both objectives.

Let us note that a fall controller is not a balance controller. A fall controller complements, and does not replace, a balance controller. Only when the default balance controller has failed to stabilize the robot, the fall controller is activated. Further, a fall controller is not a push-recovery controller. A push-recovery controller is essentially a balance controller, which specifically deals with external disturbances of larger magnitude. A robot can recover from a push e.g., through an appropriate stepping strategy.

We propose a fall strategy which rapidly modifies the fall direction of a robot in order to avoid hitting a person or an object in the vicinity. Our approach is based on the optimal modification of the support base geometry of the robot through intelligent stepping. Additional improvement to the fall controller is achieved through inertia shaping technique aimed at controlling the centroidal rotational inertia of the robot. The video is composed of two simulation animations which show the falling motion of an Asimo-like humanoid robot. In the first simulation only the footstep controller is active. In the second animation, the inertia shaping controller is activated as soon as the humanoid makes a foot touchdown.



Humanoid Fall: Powerpoint presentation

Animations

Falling motion of NAO robot: No Control
Falling motion of NAO robot: Intelligent Stepping Control
Falling motion of NAO robot: Inertia Shaping Control

A list of my papers on this topic:

  • S.-K. Yun and A. Goswami,
    Tripod Fall: Concept and Experiments of a Novel Approach to Humanoid Robot Fall Damage Reduction,
    ICRA 2014, Hongkong, China, May 2014.

    (pdf).
    This paper reports successful experimental demonstration of damage-reducing fall control strategy of humanoid robots.

    Abstract:
    This paper addresses a new control strategy to reduce the damage to a humanoid robot during a fall. Instead of following the traditional approach of finding a favorable configuration with which to fall to the ground, this method attempts to stop the robot from falling all the way to the ground. This prevents the full transfer of the robotís potential energy to kinetic energy, and consequently results in a milder impact. The controlled motion of the falling robot involves a sequence of three deliberate contacts to the ground with the swing foot and two hands, in that order. In the final configuration the robotís center of mass (CoM) remains relatively high from the floor and the robot has a relatively stable three-point contact with the ground; hence the name tripod fall. The optimal location of the three contacts are learned through reinforcement learning algorithm. The controller is simulated on a full size humanoid, and experimentally tested on the NAO humanoid robot. In this work we apply our fall controller only to a forward fall.

    Without a fall controller, when a humanoid is pushed from behind (left photo), it topples forward (middle photo) and falls on its face (right photo):

    With a fall control strategy, which in this case is lifting right foot, the humanoid can fall to the right under the same push force as above:

    With an inertia shaping fall controller, the humanoid can fall diagonally, again under the same push force as above:

    Simulation video:
    Click Here


  • Ambarish Goswami, Seung-kook Yun, Umashankar Nagarajan, Sung-Hee Lee, KangKang Yin, Shivaram Kalyanakrishnan,
    Direction-changing fall control of humanoid robots: theory and experiments,
    Autonomous Robots, Vol. 36, No. 3, March 2014.

    (pdf).
    This paper reports successful experimental demonstration of damage-reducing fall control strategy of humanoid robots.

    Abstract:
    Humanoid robots are expected to share human environments in the future and it is important to ensure the safety of their operation. A serious threat to safety is the fall of such robots, which can seriously damage the robot itself as well as objects in its surrounding. Although fall is a rare event in the life of a humanoid robot, the robot must be equipped with a robust fall strategy since the consequences of fall can be catastrophic. In this paper we present a strategy to change the default fall direction of a robot, during the fall. By changing the fall direction the robot may avoid falling on a delicate object or on a person. Our approach is based on the key observation that the toppling motion of a robot necessarily occurs at an edge of its support area. To modify the fall direction the robot needs to change the position and orientation of this edge vis-a-vis the prohibited directions. We achieve this through intelligent stepping as soon as the fall is predicted. We compute the optimal stepping location which results in the safest fall. Additional improvement to the fall controller is achieved through inertia shaping, which is a principled approach aimed at manipulating the robotís cen- troidal inertia, thereby indirectly controlling its fall direction. We describe the theory behind this approach and demonstrate our results through simulation and experiments of the Alde- baran NAO H25 robot. To our knowledge, this is the first implementation of a controller that attempts to change the fall direction of a humanoid robot.

    Without a fall controller, when a humanoid is pushed from behind (left photo), it topples forward (middle photo) and falls on its face (right photo):

    With a fall control strategy, which in this case is lifting right foot, the humanoid can fall to the right under the same push force as above:

    With an inertia shaping fall controller, the humanoid can fall diagonally, again under the same push force as above:

    Simulation video:
    Click Here


  • S.-K. Yun and A. Goswami,
    Hardware Experiments of Humanoid Robot Safe Fall using Aldebaran NAO,
    ICRA 2012, St. Paul, MN, May 2012.

    (pdf).
    This paper reports successful experimental demonstration of direction-changing fall control strategy of humanoid robots.

    Abstract:
    Although the fall of a humanoid robot is rare in controlled environments, it cannot be avoided in the real world where the robot may physically interact with the environment. Our earlier work introduced the strategy of direction-changing fall, in which the robot attempts to reduce the chance of human injury by changing its default fall direction in real-time and falling in a safer direction. The current paper reports further theoretical developments culminating in a successful hardware implementation of this fall strategy conducted on the Aldebaran NAO robot. This includes new algorithms for humanoid kinematics and Jacobians involving coupled joints and a complete estimation of the body frame attitude using an additional inertial measurement unit. Simulations and experiments are smoothly handled by our platform independent humanoid control software package called Locomote. We report experiment scenarios where we demonstrate the effectiveness of the proposed strategies in changing humanoid fall direction.

    Without a fall controller, when a humanoid is pushed from behind (left photo), it topples forward (middle photo) and falls on its face (right photo):

    With a fall control strategy, which in this case is lifting right foot, the humanoid can fall to the right under the same push force as above:

    With an inertia shaping fall controller, the humanoid can fall diagonally, again under the same push force as above:

    Simulation video:
    Click Here


  • S-H. Lee and A. Goswami,
    Fall on Backpack: Damage Minimizing Humanoid Fall on Targeted Body Segment Using Momentum Control,
    ASME 2011 8th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) inside International Design Engineering Technical Conference (IDETC), Washington DC, USA, August 2011.

    (pdf).


    Abstract:
    Safety and robustness will become critical issues when humanoid robots start sharing human environments in the future. In physically interactive human environments, a catastrophic fall is the main threat to safety and smooth operation of humanoid robots, and thus it is critical to explore how to manage an unavoidable fall of humanoids. This paper deals with the problem of reducing the impact damage to a robot associated with a fall. A common approach is to employ damage-resistant design and apply impact-absorbing material to robot limbs, such as the backpack and knee, that are particularly prone to fall related impacts. In this paper, we select the backpack to be the most preferred body segment to experience an impact. We proceed to propose a control strategy that attempts to re-orient the robot during the fall such that it impacts the ground with its backpack. We show that the robot can fall on the backpack even when it starts falling sideways. This is achieved by utilizing dynamic coupling, i.e., by rotating the swing leg aiming to generate spin rotation of the trunk (backpack), and by rotating the trunk backward to drive the trunk to touch down with the backpack. The planning and control algorithms for fall are demonstrated in simulation.

    Simulation videos:
    Without fall control, the robot would fall in a messy way on its arms: Click Here
    With fall control, the robot falls on its backpack : Click Here


  • S. Kalyanakrishnan and A. Goswami,
    Learning to Predict Humanoid Fall,
    The International Journal of Humanoid Robotics, Vol. 8, No. 2 (2011).
    (pdf).



    Abstract:
    Falls are undesirable in humanoid robots, but also inevitable, especially as robots get deployed in physically interactive human environments. We consider the problem of fall prediction: to predict if the balance controller of a robot can prevent a fall from the robotís current state. A trigger from the fall predictor is used to switch the robot from a balance maintenance mode to a fall control mode. It is desirable for the fall predictor to signal imminent falls with sufficient lead time before the actual fall, while minimizing false alarms. Analytical techniques and intuitive rules fail to satisfy these competing objectives on a large robot that is subjected to strong disturbances and exhibits complex dynamics. We contribute a novel approach to engineer fall data such that existing supervised learning methods can be exploited to achieve reliable prediction. Our method provides parameters to control the tradeoff between the false positive rate and lead time. Several combinations of parameters yield solutions that improve both the false positive rate and the lead time of hand-coded solutions. Learned solutions are decision lists with typical depths of 5Ė10, in a 16-dimensional feature space. Experiments are carried out in simulation on an ASIMO-like robot.

  • S. Kalyanakrishnan and A. Goswami,
    Predicting falls of a humanoid robot through machine learning,
    IAAI-10, Atlanta, Georgia, USA, July, 2010.

    (pdf).

    Abstract:
    Although falls are undesirable in humanoid robots, they are also inevitable, especially as robots get deployed in physically interactive human environments. We consider the problem of "fall prediction", i.e., to predict if a robot's balance controller can prevent a fall from the current state. A trigger from the fall predictor is used to switch the robot from a balance maintenance mode to a fall control mode. Hence, it is desirable for the fall predictor to signal imminent falls with sufficient lead time before the actual fall, while minimizing false alarms. Analytical techniques and intuitive rules fail to satisfy these competing objectives on a large robot that is subjected to strong disturbances and exhibits complex dynamics.

    Today effective supervised learning tools are available for finding patterns in high-dimensional data. Our paper contributes a novel approach to engineer fall data such that a supervised learning method can be exploited to achieve reliable prediction. Specifically, we introduce parameters to control the tradeoff between the false positive rate and lead time. Several parameter combinations yield solutions that improve both the false positive rate and the lead time of hand-coded solutions. Learned predictors are decision lists with typical depths of 5-10, in a 16-dimensional feature space. Experiments are carried out in simulation on an Asimo-like robot.


  • U. Nagarajan and A. Goswami,
    Generalized Direction Changing Fall Control of Humanoid Robots Among Multiple Objects,
    ICRA 2010, Anchorage, Alaska, May 2010.

    (pdf).

    Abstract:
    Humanoid robots are expected to share human environments in the future and it is important to ensure safety of their operation. A serious threat to safety is the fall of a humanoid robot, which can seriously damage both the robot and objects in its surrounding. This paper proposes a strategy for planning and control of fall. The controller's objective is to prevent the robot from hitting surrounding objects during a fall by modifying its default fall direction.

    We have earlier presented such a direction-changing fall controller, see here. However, the controller was applicable only when the robot's surrounding contained a single object. In this paper we introduce a generalized approach to humanoid fall-direction control among multiple objects. This new framework algorithmically establishes a desired fall direction through assigned scores, considers a number of control options, and selects and executes the best strategy. The fall planner is also able to select "No Action" as the best strategy, if appropriate. The controller is interactive and is applicable for fall occurring during upright standing or walking. The fall performance is continuously tracked and can be improved in real-time. The planning and control algorithms are demonstrated in simulation on an ASIMO-like humanoid robot.

  • Animation: Direction Changing Humanoid Fall

  • S.-K. Yun, A. Goswami and Y. Sakagami,
    Safe Fall: Humanoid robot fall direction change through intelligent stepping and inertia shaping,
    ICRA 2009, Kobe, Japan, May 2009.

    (pdf).

    Abstract:
    Although fall is a rare event in the life of a humanoid robot, we must be prepared for it because its consequences are serious. In this paper we present a fall strategy which rapidly modifies the robot's fall direction in order to avoid hitting a person or an object in the vicinity. Our approach is based on the key observation that during "toppling" the rotational motion of a robot necessarily occurs at the leading edge or the leading corner of its support base polygon. To modify the fall direction the robot needs to change the position and orientation of this edge or corner vis-a-vis the prohibited direction. We achieve it through intelligent stepping as soon as a fall is detected. We compute the optimal stepping location which results in the safest fall. Additional improvement to the fall controller is achieved through inertia shaping techniques aimed at controlling the centroidal inertia of the robot.

    We demonstrate our results through the simulation of an ASIMO-like humanoid robot. To our knowledge, this is the first implementation of a controller that attempts to change the fall direction of a humanoid robot.
    humanoid robot under external force humanoid robot falls on object if there is no fall controller humanoid robot successfully avoids 
	object with our fall controller

  • Back to Current Research
    Back to Main Page

    Page last updated September 1, 2014