ANKITMEHTA

Safe Bedside Manipulation in Geriatric Care

A simulated two-arm robot helps a caregiver change an incontinence brief on a care bed. The robot assists. It does not replace the caregiver.

Most safety work bounds the force pushed into a surface. For aging skin that is the wrong quantity. Skin tears from sideways drag well below the push limit. Sliding a brief out from under a body is drag. So I estimate shear in real time and check it against a second sensor in the bed. Furthermore I am attempting to design a care-giving bed that works in conjunction with the robot.

The recovery system also has to know which steps it can undo. Stopping is not always the safe move.

Learned Gap Density Estimation for Reactive Navigation

Dynamic Gap steers a robot through the gaps between pedestrians. Its Kalman filter assumes linear obstacle motion. It also puts false velocity on static obstacles. We have explored using a GRU to correct this behavior.

Density alone is a snapshot. In one collision the chosen gap went from 0.12 to 2.79 in six planning cycles. By then the robot had committed. So I added two features. One is the rate of change of density. The other is aspect ratio, 1/θ, which tells a corridor apart from an opening.

For one experiment, I ran three configurations over 50 episodes each. With the planner config held fixed, the future-density model cut collision events by 30%. The goal rate rose from 53% to 67%. No episode got worse.

Predictions tracked ground truth. Navigation still got worse. Walls carry near-zero density, so minimising it pulled the robot into them. The planner also blew its 10 Hz compute budget and fell back to idling. Neither problem was in the network. Retuning three planner weights cut the mean path from 52.7 m to 30.0 m.

I also rewrote two gap cost functions. The old width term had no ceiling and reached 540 at a 1 mm gap. It now rejects gaps the robot cannot fit through, then prices the clearance that is left.

Decentralised Two-Quadruped Search and Rescue

Two quadrupeds search an 11 m arena of 18 trees for a moving target. There is no central controller and no shared memory. Each agent has its own gait control, eight IR rays, and a private belief. They exchange belief over UDP at 10 Hz and fuse it by covariance intersection.

So far, I've found and fixed six defects. Most originated from unexpected places. One check decided when the robot could say it had found the target. It mixed position uncertainty with speed uncertainty, and speed uncertainty is always far larger. The total never fell below the threshold, so the robot kept reporting a search it had already finished.

Frontier navigation also failed and a fixed waypoint approach replaced it. That works in this arena. It cannot work in an unexplored one so fixing that is the next problem.