Humanoid-universe.com

<t>Slight correction, though the overall point stands:

Sensor fusion mostly earns its keep by covering for each individual sensor's weaknesses - vision struggles with occlusion and lighting, IMUs drift, force/torque sensors are noisy at low loads - fusing them gives a more robust estimate than ...

<t>From hands-on experience,

'Zero-shot sim-to-real' rarely means literally zero real-world tuning in practice - it usually means the policy transfers well enough to be usable with only calibration and minor safety-limit adjustments, rather than needing a full additional training phase on ...

<t>Not sure I fully agree here.

Vision-Language-Action (VLA) models like RT-2, OpenVLA, and Physical Intelligence's pi0 unify a vision-language backbone with an action-output head, letting a robot map a camera image and a text instruction directly to motor commands instead of hand-coding separate ...

<t>@samuel.adams Slight correction, though the overall point stands:

Estimating joint torque from motor current draw is cheap and requires no extra sensor, but it's less accurate than a dedicated torque sensor because it doesn't capture friction losses through the gearbox - good enough for coarse ...

<t>@emily.kumar Just to be precise about one thing:

Tactile skin arrays have improved a lot, but 'good enough to matter' really depends on the task - coarse contact detection across a large area is fairly mature, while fine, high-resolution force distribution sensing (like a human fingertip) is ...

<t>To answer this directly:

IMU drift over time (bias instability) is usually the real culprit behind slowly diverging state estimates, not noise - it's typically handled with sensor fusion against other references (visual odometry, joint kinematics) rather than trying to eliminate drift at the ...

<t>I dealt with almost this exact situation.

Bimanual manipulation - two arms coordinating on one task - is harder than it looks mostly because of the added degrees of freedom and the timing/force coordination required; a lot of 'two-handed' demos are actually closer to two independent single ...

<t>This lines up with my experience.

How much ML background a controls role actually requires varies a lot by company and team - some whole-body control positions remain heavily classical-optimization-focused, while others increasingly expect familiarity with reinforcement learning or imitation ...

<t>@smartinez Here's what I know on this:

A commonly cited skill gap in new-grad applicants is practical systems integration experience - many candidates are individually strong in ML or in mechanical design, but comparatively few have hands-on experience getting perception, planning, and control ...

<t>Thanks for laying this out, genuinely useful.

Sim-to-real transfer still commonly breaks on contact dynamics - friction, restitution, and deformable/compliant surfaces are the hardest things to model accurately in simulation, so policies trained purely in sim often need real-world fine-tuning ...