Rethinking Robot Safety: Moving Beyond the Binary If robot safety was ever a yes-or-no question it can't be any longer. When people ask whether a humanoid robot is safe, they usually want a clean answer. The difficulty is that no complex autonomous system can really offer one. Some level of risk is always present. When safety is treated as a single gate that a system either passes or fails, the conversation tends to stall. We end up with debates about whether a robot is "ready" in the abstract, when the more useful questions are about the specific context in which it operates, the kinds of harm that are possible there, and how confident we are that we've measured and managed them. In 2023, @Waymo published a detailed framework organized around the idea of "absence of unreasonable risk." The phrasing accepts that some residual risk will always remain, and it shifts the work toward establishing what counts as an acceptable level of risk in a given context, and then demonstrating that the system meets it. It guides how Waymo assesses risk along several dimensions: Severity potential — the scale and extent of the harm that could occur Conflict role — whether the system initiated a hazardous situation or was responding to one created by others Behavioral capability — distinguishing among regulatory compliance, conflict avoidance, and collision avoidance Functionality status — performance under normal conditions versus performance when something is degraded Level of aggregation — reasoning about individual events alongside statistical, fleet-wide rates Each dimension can be measured, and acceptance criteria can be set for each. Waymo describes this as methodology-agnostic, meaning the broad approach could in principle be adopted by others working in different parts of the autonomous systems world. Look, I just got here, but it strikes me as correct to treat safety as something you build a credible, evidence-backed case for, across many dimensions, rather than a single threshold you clear once. @Figure_robot has established a Center for the Advancement of Humanoid Robot Safety and has committed to publishing periodic reports covering its testing procedures, results, and challenges. The company's safety lead has been candid that the standards humanoid manufacturers are currently required to meet remain ambiguous, given that there is no dedicated regulatory regime for this class of machine yet.Some of the companies building humanoid robots appear to be moving in a similar direction. Publishing testing methods and acknowledging open challenges is consistent with the spirit of the Waymo approach: building trust through transparency and a documented safety case rather than a one-time declaration. It reflects an understanding that safety in this space is an ongoing, evidence-driven undertaking. Other developers seem to be working from related premises. Another company building a humanoid for industrial settings has described leaning on the established safety standards that already exist for mobile manipulators and autonomous mobile robots, while anticipating that humanoid-specific standards will emerge over time. Its robots incorporate perception-based behaviors that slow or stop the machine when a person enters a defined zone. That’s more of a concrete way of managing risk according to context rather than asserting blanket safety. The common thread, as I read it, is an effort to ground safety claims in defined criteria and operating conditions, and to treat international standards as a foundation to build on as they develop. From here, the industry could wait for regulators to produce a clear, binary certification gate. Think of some official stamp that a humanoid is approved for a given environment. That has obvious appeal in its simplicity but clearly that solution has many problems. It may take years to arrive, could freeze design around whatever assumptions were current when it was written, and might prove ill-suited to a fast-moving field. On the other hand, the industry could work now to build out spectrum-style frameworks, grounded in existing manufacturing and functional safety standards and in honest measurement of residual risk. This is harder and less tidy, asks more of the companies doing it, and asks observers to get comfortable with safety claims that are conditional and contextual. The conceptual approach to safety deserves as much attention as the engineering itself. The framework we reach for shapes which questions get asked, what evidence gets collected, and ultimately what gets deployed. For humanoid robots that will operate in warehouses, factories, stores, and eventually homes, the credibility of the safety case is what earns the trust everything else depends on. Treating safety as table stakes doesn't require pretending a robot is perfectly safe. It requires a credible, transparent, multi-dimensional account of how risk is being measured and managed in the contexts where the machine actually works. The companies that seem to take this seriously tend to define their criteria explicitly, measure across several dimensions, share evidence of how they're performing, and stay engaged with the standards bodies shaping the field. As operating data accumulates, their safety case can grow more confident over time. Again, the binary question of whether a robot is "safe" is too blunt for the systems now being built, and the spectrum-based frameworks emerging from autonomous vehicles seem to offer a more honest and more workable way to reason about it. One of the more important conversations the humanoid robotics field can have right now is how we conceive of safety in the first place.

Despite reportedly “rethinking exclusivity” and making exclusives a major topic on Player’s Voice, Xbox has confirmed it will continue showing PlayStation logos during next week’s Xbox Showcase for games also releasing on PS5. Xbox has previously said it wants to be “transparent” about where games are available, which is why platform logos for PlayStation, Nintendo, and Steam continue to appear during its presentations.

Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers. 🔹 Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth. 🔹 Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale. 🔹 Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead. 🔹 Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains. 🔗Full report: