Spent the last few weeks on a benchmark/harness that tries to answer one question honestly: did a robot arm actually do the demonstrated task, or did the success metric just get fooled? The setup: compile a human demo into an object-centric graph (what changed in the world: relations, contacts, event order), run a solver, then independently extract a graph from the rollout only and check if they match. The whole point is a hard information boundary so the "answer key" can never leak into the side that grades the rollout. A no-op baseline fails with named failure classes; a dumb scripted arm passes. That contrast is the thing I care about. Most manipulation success metrics are hand-coded predicates written by the same person training the policy. The policy author controls both the behavior and the definition of "success." That's a conflict of interest we'd never accept in ML benchmarking, yet it's standard in manipulation eval. But I keep going back and forth on whether this matters, and I'd like other people's read: The case that it's real: VLA/foundation-model training is starved for reliable dense reward at scale. Human raters don't scale, brittle predicates lie. An automatic, embodiment-agnostic grader that can say "this rollout reproduced the demonstrated transformation, here's why it failed" seems like an obviously-missing piece of the training loop. The case that it's a non-problem: maybe everyone's already fine with task-specific success checks because in practice you only care about the tasks you're shipping, and a general verifier is solving for a generality nobody needs. And the representation that makes verification tractable (discrete relational state — INSIDE/TOUCHING/event-order) is also what caps it: it handles pick/place/insert/open-drawer but has no obvious purchase on force-profile or deformable tasks, which is exactly where the frontier is. There's also the uncomfortable bit: the hard 80% is perception (video → graph under occlusion and contact noise), and that's where the leakage discipline gets harder, not easier, because your extractor is now a learned, error-prone thing. Two questions I don't have a settled answer on: Is reward/eval honesty a first-order bottleneck for the current generation of manipulation learning, or second-order polish? Is object-centric relational state a dead representation for where manipulation is actually going, or a reasonable floor you build up from? submitted by /u/Alexpplay [link] [Kommentare]
In a bid to dismiss a lawsuit over xAI’s polluting gas turbines, the Justice Department claimed the company is integral to military operations—including the Iran War.
This is because yesterday my friend told me one of his wallets with 22k$ worth of usdt tether got blacklisted, frozen now as he is in india we don't know what to do ? This defeats the entire purpose of holding stablecoins or crypto We tried contacting tether and the relevant authorities but no good they just told us its cause of aml and we don't know where to find a lawyer (maybe his fee would be more than this?) Kindly suggest any cheap solutions to this submitted by /u/Skbhuvai [link] [Kommentare]
Anthropic has delayed subscription billing changes for third-party Claude tools, so you can keep using your Claude plan in Conductor with no additional limitations.
Monarch Tractor, a startup founded in 2018 with promises of saving farmers time and money, collapsed after customers reported its tractor could not operate autonomously.
