Avoiding obstacles poses a significant challenge for amputees using mechanically-passive transfemoral prosthetic limbs due to their lack of direct knee control. In contrast, powered prostheses can potentially improve obstacle avoidance via their ability to add energy to the system. In past work, researchers have proposed stumble recovery systems for powered prosthetic limbs that provide assistance in the event of a trip. However, these systems only aid recovery after an obstacle has disrupted the user's gait and do not proactively help the amputee avoid obstacles. To address this problem, we designed an adaptive system that learns online to use kinematic data from the prosthetic limb to detect the user's obstacle avoidance intent in early swing. When the system detects an obstacle, it alters the planned swing trajectory to help avoid trips. Additionally, the system uses a regression model to predict the required knee flexion angle for the trip response. We validated the system by comparing obstacle avoidance success rates with and without the obstacle avoidance system. For a non-amputee subject wearing the prosthesis through an adapter, the trip avoidance system improved the obstacle negotiation success rate from 37% to 89%, while an amputee subject improved his success rate from 35% to 71% when compared to utilizing minimum jerk trajectories for the knee and ankle joints.

We propose a method for optimizing control policies for assistive lower-limb devices. The method frames parameter selection as a dueling bandits problem in which a user indicates his or her qualitative preferences between pairs of parameter sets chosen from a library. We generate the library through an offline optimization procedure that seeks to reproduce the varied gaits of healthy human subjects. By separating the parameter selection process into online and offline portions, the method can handle high-dimensional parameter spaces and produces policies that can generalize to different gait scenarios such as speed variation. We evaluate the method on five subjects walking on a powered knee and ankle prosthesis governed by a neuromuscular control policy that has 43 parameters. We find the five subjects preferred four different parameter sets from the library and that the resulting optima resemble intact subject gait data. This result suggests the offline portion of the optimization method indeed produces control parameters that can adapt to different gaits. Moreover, we find that for three out of the four parameter sets we tested, the procedure also generates parameters that improve the ability of the prosthesis to adapt to increasing gait speed by increasing ankle net work production. The results encourage further research and exploration in clinical settings toward advanced prosthesis controls that employ online learning.

Transfemoral amputees often suffer from falls and a fear of falling that leads to a decreased quality of life. Existing control strategies for powered knee-ankle prostheses demonstrate only limited ability to react to disturbances that induce falls such as trips, slips, and obstacles. In contrast, prior work on neuromuscular modeling of human locomotion suggests that control strategies based on local reflexes exhibit robustness to unobserved terrain such as slopes and steps. Therefore, we propose that a powered knee-ankle prosthesis governed by reflexive local controls will more competently adapt to unperceived disturbances. To test this hypothesis, we simulate a neuromuscular model of a transfemoral amputee walking over rough ground with a powered knee-ankle prosthesis governed by the proposed reflexive controller. We show that the proposed control allows the amputee to walk farther over rough ground than does the state-of-the-art control. The proposed controller also more readily rejects deviations from nominal walking gaits such as those encountered during a trip. These results suggest that applying the proposed control to a powered knee-ankle prosthesis will substantially improve amputee gait stability.

JPL has developed the worlds first rock climbing robot. This video presents climbing trials at vertical, overhanging, and inverted angles, and a zero-g drill for astronauts.

The video presents microspine-based anchors being developed for gripping rocks on the surfaces of comets and asteroids, or for use on cliff faces and lava tubes on Mars. Two types of anchor prototypes are shown on supporting forces in all directions away from the rock; >;160 N tangent, >;150 N at 45°, and >;180 N normal to the surface of the rock. A compliant robotic ankle with two active degrees of freedom interfaces these anchors to the Lemur IIB robot for future climbing trials. Finally, a rotary percussive drill is shown coring into rock regardless of gravitational orientation. As a harder-than-zero-g proof of concept, inverted drilling was performed creating 20mm diameter boreholes 83 mm deep in vesicular basalt samples while retaining 12 mm diameter rock cores in 3-6 pieces.

To grip rocks on the surfaces of asteroids and comets, and to grip the cliff faces and lava tubes of Mars, a 250 mm diameter omni-directional anchor is presented that utilizes a hierarchical array of claws with suspension flexures, called microspines, to create fast, strong attachment. Prototypes have been demonstrated on vesicular basalt and a'a lava rock supporting forces in all directions away from the rock. Each anchor can support >;160 N tangent, >;150 N at 45°, and >;180 N normal to the surface of the rock. A two-actuator selectively-compliant ankle interfaces these anchors to the Lemur IIB robot for climbing trials. A rotary percussive drill was also integrated into the anchor, demonstrating self-contained rock coring regardless of gravitational orientation. As a harder-than-zero-g proof of concept, 20mm diameter boreholes were drilled 83 mm deep in vesicular basalt samples, retaining a 12 mm diameter rock core in 3-6 pieces while in an inverted configuration, literally drilling into the ceiling.