TacForeSight: Predictive Robotics & Adaptive Optics

• TacForeSight is an advanced predictive framework that uses sensor fusion and latent dynamics modeling to forecast tactile and force signals in real-time.
• In robotics, it employs a cascaded two-stage architecture with cross-attention to enable proactive contact reasoning and 20 Hz control on commodity hardware.
• Applied in adaptive optics, its AR forecasting model significantly reduces lag errors in wavefront correction, enhancing tip/tilt and high-order modal performance.

TacForeSight encompasses advanced predictive frameworks for robotics and adaptive optics, each leveraging data-driven, real-time forecasting to proactively mitigate delays and perturbations in complex physical systems. Across these application domains, TacForeSight achieves robust performance by integrating compact latent models, sensor fusion, and sequence modeling to forecast critical states or control signals, thereby enabling precise, anticipatory control in high-frequency regimes (Zang et al., 9 Jun 2026, Hafeez et al., 2021).

1. Framework for Contact-Rich Robotic Manipulation

TacForeSight for robotic manipulation comprises a cascaded, two-stage architecture designed explicitly for real-time control in environments with dynamic contact transitions and complex surface geometries (Zang et al., 9 Jun 2026).

• TacForceWM (Force-Conditioned Tactile World Model): Learns a low-dimensional latent representation of fingertip contact states from dual-finger tactile images, conditioned on high-frequency wrist force/torque signals. The model forecasts the short-horizon future tactile latent dynamics, capturing the asymmetric spatiotemporal influence of global force and localized tactile feedback.
• Predictive Tactile-Conditioned Policy: Uses a lightweight flow-matching policy to transform the predicted tactile latents into anticipatory contact priors. A cross-attention mechanism explicitly links current and forecasted tactile latents, while a tactile-guided gating module adaptively fuses visuo-tactile representations. At inference, TacForceWM predicts future tactile latents in parallel; the policy consumes these predictions along with current multimodal observations to generate 20 Hz control actions on commodity hardware (RTX 4090).

This architecture enables proactive contact reasoning by allowing the robot to anticipate and adapt to imminent contact events and transitions within a highly compact latent space, thereby ensuring both robustness and computational efficiency.

2. Mathematical Formulation and Latent Dynamics Modeling

Let $o_t$ denote the raw multimodal observation at time $t$, with tactile images $X_t$, wrist wrench $w_t$, camera image $I_t$, and proprioceptive state $s_t$. The formulation is as follows:

• Tactile Tokenization: $z_t = E_\mathrm{tac}(X_t) \in \mathbb{R}^{D_z}$
• Force Sequence Encoding: $$c_{t-H:t} = G_\phi(w_{t-nH:t}) \in \mathbb{R}^{H \times D_c}$$
• Latent Chunk Forecasting: $$\hat{Z}_{t}^{\mathrm{tac}} = T_\psi(Z_{t-H:t}^{\mathrm{tac}}, c_{t-H:t})$$, producing a future chunk $[\hat{z}_{t-H+\Delta}, \ldots, \hat{z}_{t+\Delta}]$

TacForceWM is supervised with a compound prediction loss $t$0 (MSE in latent values and first-order dynamics) and employs a Sketched Isotropic Gaussian Regularizer (SIGReg) to avoid representation collapse:

$t$1

The policy integrates current tactile latents $t$2 and the forecast $t$3 using cross-attention, pooled into $t$4. Visual and tactile features are fused by a learned gating function $t$5, yielding the composite feature $t$6. The action head is trained via a flow-matching objective $t$7.

3. Network Architecture and Implementation

Tactile Encoder: Inputs dual 35×20×3 displacement maps, processed by a ResNet-style CNN, enriched with 2D positional and finger-ID embeddings. The result is flattened, [CLS] prepended, and fed to a 6-layer Transformer (8 heads, hidden dim $t$8), producing the tactile latent $t$9.

Force Encoder: Processes high-rate wrench data using a linear projection, followed by a causal dilated 1D-convolution stack, downsampling to $X_t$0 time steps ($X_t$1).

Latent Dynamics Predictor: A 4-layer Transformer integrates tactile latent history and force encoding, employing adaptive LayerNorm (AdaLN) conditioned on force. The network outputs a prediction chunk over the future horizon.

Policy Architecture:

• Visual encoder: frozen DINOv2-small producing $X_t$2
• Proprioception: MLP applied to recent proprioceptive state
• Cross-attention links current and future tactile latents
• Tactile-guided gating fuses visual/tactile states
• Flow-matching action head (U-Net based) drives control outputs

Policy operates at 20 Hz; with $X_t$3, $X_t$4, all-attention and gating computations remain under a 50 ms control cycle on a single RTX 4090 (Zang et al., 9 Jun 2026).

4. Training Methodology and Loss Functions

TacForceWM Pretraining:

• Loss: $X_t$5, with $X_t$6, $X_t$7
• Dataset: 2,700 real-world episodes containing both nominal and diverse contact events
• Optimizer: Adam, learning rate $X_t$8, batch size 64, 150k steps

Policy Training:

• Stage 2 finetuning (imitation): TacForceWM is frozen; policy is trained on expert action chunks (nominal and recovery demonstrations) using AdamW, learning rate $X_t$9, batch size 32, for 200k steps
• Minor random force perturbations as the only augmentation

5. Real-Time Execution and System Integration

TacForeSight’s real-time capability is achieved through latent-space forecasting and efficient neural computation. Both TacForceWM and the policy execute asynchronously at 20 Hz, forecasting entirely within a compact 256-dimensional latent space. Memory and compute requirements are minimized: cross-attention and gating incur $w_t$0 cost per step, enabling the end-to-end system to maintain a 50 ms control cycle on modern GPUs (Zang et al., 9 Jun 2026).

6. Experimental Benchmarks and Ablation Findings

TacForeSight was evaluated on five contact-rich tasks with 20 trials each—vase wiping, card swiping, tube adjustment & insertion, bulb insertion & locking, and wire insertion—plus three perturbation-based variants. Quantitative results:

Policy	Wipe	Swipe	Adj.	Lock	Insert	W-P	S-P	A-P
DP [vision]	70%	35%	30%	10%	15%	0%	0%	0%
DP+Tac+Frc	80%	40%	35%	30%	15%	25%	0%	35%
KineDex	30%	35%	25%	45%	30%	10%	0%	0%
FoAR	50%	50%	35%	25%	20%	30%	0%	25%
RDP (reactive)	85%	50%	25%	55%	0%	35%	65%	0%
Ours	100%	85%	70%	80%	60%	90%	85%	85%

Ablation studies indicate:

• World model conditioning: Adding wrist-wrench signals substantially improved forecasting (MSE↓, Cosine↑, KL_sym↓).
• Policy variants: Exclusion of predicted tactile latents or cross-attention severely degrades robustness to perturbations.

The gating mechanism's learned weight $w_t$1 aligns with contact-force uncertainty, increasing tactile reliance during ambiguous interactions.

7. Discussion, Limitations, and Perspectives

TacForeSight enables anticipatory manipulation by explicitly modeling the lead-lag relationship between force and tactile feedback—global force changes forecast imminent local deformations, providing a predictive window (100–200 ms) for corrective or preparatory action at contact transitions (e.g., slip onset). The latent-dynamics approach affords computational tractability; pixel-level or video-based prediction would be prohibitive for real-time control.

Key ablation results confirm that wrist-wrench-conditioned forecasting and explicit cross-attention are essential for resilience under dynamic disturbance. The short prediction horizon ($w_t$2) is a current limitation; longer or multi-modal (e.g., visual or force) forecasts, as well as integration of geometric or language priors, are identified as promising directions.

TacForeSight thus establishes a new paradigm in contact-rich robotic manipulation, with state-of-the-art robustness and efficiency on challenging real-robot tasks (Zang et al., 9 Jun 2026).

8. Forecasting in Adaptive Optics Systems

In adaptive optics, TacForeSight refers to a data-driven, linear auto-regressive (AR) forecasting filter inserted in the real-time control (RTC) of wavefront-correction commands. The AR model predicts future tip/tilt or high-order modal coefficients based on recent command history, targeting reduction of lag error due to sensor and processing delays (Hafeez et al., 2021):

• Model Structure: For each control channel,

$w_t$3

where $w_t$4 and $w_t$5 are estimated by ordinary least squares (OLS) over recent telemetry ($w_t$6 samples).

• Model order selection: $w_t$7 for Tip/Tilt, $w_t$8 for high-order modes, as determined by autocorrelation analysis and diminishing returns in RMSE.
• Performance: At 1 kHz, single-frame-ahead AR(30) reduces tip/tilt RMS error by $w_t$9, high-order AR(5) by $I_t$0 versus "Echo" baseline ($I_t$1). The method is robust up to several ms forecast horizons. Nonlinear neural sequence models (LSTM, WaveNet) produced no further improvement.

The AR filter is computationally trivial compared to wavefront reconstructions and is a practical, easily retrofitted upgrade for existing AO systems.

TacForeSight, in both robotics and adaptive optics, exemplifies the practical impact of efficiently learning and leveraging latent temporal structure for proactive, real-time control in complex, disturbance-prone environments (Zang et al., 9 Jun 2026, Hafeez et al., 2021).